Computer architecture for emulating a bidirectional string correlithm object generator in a correlithm object processing system

ABSTRACT

A device configured to emulate a bidirectional string correlithm object generator includes multiple processing stages that operate together to output a bidirectional string correlithm object. The bidirectional string correlithm object includes sub-string correlithm objects that extend in different n-dimensional directions from a central sub-string correlithm object.

TECHNICAL FIELD

The present disclosure relates generally to computer architectures foremulating a processing system, and more specifically to a computerarchitecture for emulating a bidirectional string correlithm objectgenerator in a correlithm object processing system.

BACKGROUND

Conventional computers are highly attuned to using operations thatrequire manipulating ordinal numbers, especially ordinal binaryintegers. The value of an ordinal number corresponds with its positionin a set of sequentially ordered number values. These computers useordinal binary integers to represent, manipulate, and store information.These computers rely on the numerical order of ordinal binary integersrepresenting data to perform various operations such as counting,sorting, indexing, and mathematical calculations. Even when performingoperations that involve other number systems (e.g. floating point),conventional computers still resort to using ordinal binary integers toperform any operations.

Ordinal based number systems only provide information about the sequenceorder of the numbers themselves based on their numeric values. Ordinalnumbers do not provide any information about any other types ofrelationships for the data being represented by the numeric values suchas similarity. For example, when a conventional computer uses ordinalnumbers to represent data samples (e.g. images or audio signals),different data samples are represented by different numeric values. Thedifferent numeric values do not provide any information about howsimilar or dissimilar one data sample is from another. Unless there isan exact match in ordinal number values, conventional systems are unableto tell if a data sample matches or is similar to any other datasamples. As a result, conventional computers are unable to use ordinalnumbers by themselves for comparing different data samples and insteadthese computers rely on complex signal processing techniques.Determining whether a data sample matches or is similar to other datasamples is not a trivial task and poses several technical challenges forconventional computers. These technical challenges result in complexprocesses that consume processing power which reduces the speed andperformance of the system. The ability to compare unknown data samplesto known data samples is crucial for many security applications such asfacial recognition, voice recognition, and fraud detection.

Thus, it is desirable to provide a solution that allows computingsystems to efficiently determine how similar different data samples areto each other and to perform operations based on their similarity.

SUMMARY

Conventional computers are highly attuned to using operations thatrequire manipulating ordinal numbers, especially ordinal binaryintegers. The value of an ordinal number corresponds with its positionin a set of sequentially ordered number values. These computers useordinal binary integers to represent, manipulate, and store information.These computers rely on the numerical order of ordinal binary integersrepresenting data to perform various operations such as counting,sorting, indexing, and mathematical calculations. Even when performingoperations that involve other number systems (e.g. floating point),conventional computers still resort to using ordinal binary integers toperform any operations.

Ordinal based number systems only provide information about the sequenceorder of the numbers themselves based on their numeric values. Ordinalnumbers do not provide any information about any other types ofrelationships for the data being represented by the numeric values suchas similarity. For example, when a conventional computer uses ordinalnumbers to represent data samples (e.g. images or audio signals),different data samples are represented by different numeric values. Thedifferent numeric values do not provide any information about howsimilar or dissimilar one data sample is from another. Unless there isan exact match in ordinal number values, conventional systems are unableto tell if a data sample matches or is similar to any other datasamples. As a result, conventional computers are unable to use ordinalnumbers by themselves for comparing different data samples and insteadthese computers rely on complex signal processing techniques.Determining whether a data sample matches or is similar to other datasamples is not a trivial task and poses several technical challenges forconventional computers. These technical challenges result in complexprocesses that consume processing power which reduces the speed andperformance of the system. The ability to compare unknown data samplesto known data samples is crucial for many applications such as securityapplication (e.g. face recognition, voice recognition, and frauddetection).

The system described in the present application provides a technicalsolution that enables the system to efficiently determine how similardifferent objects are to each other and to perform operations based ontheir similarity. In contrast to conventional systems, the system usesan unconventional configuration to perform various operations usingcategorical numbers and geometric objects, also referred to ascorrelithm objects, instead of ordinal numbers. Using categoricalnumbers and correlithm objects on a conventional device involveschanging the traditional operation of the computer to supportrepresenting and manipulating concepts as correlithm objects. A deviceor system may be configured to implement or emulate a special purposecomputing device capable of performing operations using correlithmobjects. Implementing or emulating a correlithm object processing systemimproves the operation of a device by enabling the device to performnon-binary comparisons (i.e. match or no match) between different datasamples. This enables the device to quantify a degree of similaritybetween different data samples. This increases the flexibility of thedevice to work with data samples having different data types and/orformats, and also increases the speed and performance of the device whenperforming operations using data samples. These technical advantages andother improvements to the device are described in more detail throughoutthe disclosure.

In one embodiment, the system is configured to use binary integers ascategorical numbers rather than ordinal numbers which enables the systemto determine how similar a data sample is to other data samples.Categorical numbers provide information about similar or dissimilardifferent data samples are from each other. For example, categoricalnumbers can be used in facial recognition applications to representdifferent images of faces and/or features of the faces. The systemprovides a technical advantage by allowing the system to assigncorrelithm objects represented by categorical numbers to different datasamples based on how similar they are to other data samples. As anexample, the system is able to assign correlithm objects to differentimages of people such that the correlithm objects can be directly usedto determine how similar the people in the images are to each other. Inother words, the system can use correlithm objects in facial recognitionapplications to quickly determine whether a captured image of a personmatches any previously stored images without relying on conventionalsignal processing techniques.

Correlithm object processing systems use new types of data structurescalled correlithm objects that improve the way a device operates, forexample, by enabling the device to perform non-binary data setcomparisons and to quantify the similarity between different datasamples. Correlithm objects are data structures designed to improve theway a device stores, retrieves, and compares data samples in memory.Correlithm objects also provide a data structure that is independent ofthe data type and format of the data samples they represent. Correlithmobjects allow data samples to be directly compared regardless of theiroriginal data type and/or format.

A correlithm object processing system uses a combination of a sensortable, a node table, and/or an actor table to provide a specific set ofrules that improve computer-related technologies by enabling devices tocompare and to determine the degree of similarity between different datasamples regardless of the data type and/or format of the data samplethey represent. The ability to directly compare data samples havingdifferent data types and/or formatting is a new functionality thatcannot be performed using conventional computing systems and datastructures.

In addition, correlithm object processing system uses a combination of asensor table, a node table, and/or an actor table to provide aparticular manner for transforming data samples between ordinal numberrepresentations and correlithm objects in a correlithm object domain.Transforming data samples between ordinal number representations andcorrelithm objects involves fundamentally changing the data type of datasamples between an ordinal number system and a categorical number systemto achieve the previously described benefits of the correlithm objectprocessing system.

Using correlithm objects allows the system or device to compare datasamples (e.g. images) even when the input data sample does not exactlymatch any known or previously stored input values. For example, an inputdata sample that is an image may have different lighting conditions thanthe previously stored images. The differences in lighting conditions canmake images of the same person appear different from each other. Thedevice uses an unconventional configuration that implements a correlithmobject processing system that uses the distance between the data sampleswhich are represented as correlithm objects and other known data samplesto determine whether the input data sample matches or is similar to theother known data samples. Implementing a correlithm object processingsystem fundamentally changes the device and the traditional dataprocessing paradigm. Implementing the correlithm object processingsystem improves the operation of the device by enabling the device toperform non-binary comparisons of data samples. In other words, thedevice can determine how similar the data samples are to each other evenwhen the data samples are not exact matches. In addition, the device canquantify how similar data samples are to one another. The ability todetermine how similar data samples are to each other is unique anddistinct from conventional computers that can only perform binarycomparisons to identify exact matches.

A string correlithm object comprising a series of adjacent sub-stringcorrelithm objects whose cores overlap with each other to permit datavalues to be correlated with each other in n-dimensional space. Thedistance between adjacent sub-string correlithm objects can be selectedto create a tighter or looser correlation among the elements of thestring correlithm object in n-dimensional space. Thus, where data valueshave a pre-existing relationship with each other in the real-world,those relationships can be maintained in n-dimensional space if they arerepresented by sub-string correlithm objects of a string correlithmobject. In addition, new data values can be represented by sub-stringcorrelithm objects by interpolating the distance between those and otherdata values and representing that interpolation with sub-stringcorrelithm objects of a string correlithm object in n-dimensional space.The ability to migrate these relationships between data values in thereal world to relationships among correlithm objects provides asignificant advance in the ability to record, store, and faithfullyreproduce data within different computing environments. Furthermore, theuse of string correlithm objects significantly reduces the computationalburden of comparing time-varying sequences of data, or multi-dimensionaldata objects, with respect to conventional forms of executing dynamictime warping algorithms. The reduced computational burden results infaster processing speeds and reduced loads on memory structures used toperform the comparison of string correlithm objects.

The problems associated with comparing data sets and identifying matchesbased on the comparison are problems necessarily rooted in computertechnologies. As described above, conventional systems are limited to abinary comparison that can only determine whether an exact match isfound. Emulating a correlithm object processing system provides atechnical solution that addresses problems associated with comparingdata sets and identifying matches. Using correlithm objects to representdata samples fundamentally changes the operation of a device and how thedevice views data samples. By implementing a correlithm objectprocessing system, the device can determine the distance between thedata samples and other known data samples to determine whether the inputdata sample matches or is similar to the other known data samples. Inaddition, the device can determine a degree of similarity thatquantifies how similar different data samples are to one another.

Certain embodiments of the present disclosure may include some, all, ornone of these advantages. These advantages and other features will bemore clearly understood from the following detailed description taken inconjunction with the accompanying drawings and claims.

BRIEF DESCRIPTION OF THE DRAWINGS

For a more complete understanding of this disclosure, reference is nowmade to the following brief description, taken in connection with theaccompanying drawings and detailed description, wherein like referencenumerals represent like parts.

FIG. 1 is a schematic view of an embodiment of a special purposecomputer implementing correlithm objects in an n-dimensional space;

FIG. 2 is a perspective view of an embodiment of a mapping betweencorrelithm objects in different n-dimensional spaces;

FIG. 3 is a schematic view of an embodiment of a correlithm objectprocessing system;

FIG. 4 is a protocol diagram of an embodiment of a correlithm objectprocess flow;

FIG. 5 is a schematic diagram of an embodiment a computer architecturefor emulating a correlithm object processing system;

FIG. 6 illustrates an embodiment of how a string correlithm object maybe implemented within a node by a device;

FIG. 7 illustrates another embodiment of how a string correlithm objectmay be implemented within a node by a device;

FIG. 8 is a schematic diagram of another embodiment of a deviceimplementing string correlithm objects in a node for a correlithm objectprocessing system;

FIG. 9 is an embodiment of a graph of a probability distribution formatching a random correlithm object with a particular correlithm object;

FIG. 10 is a schematic diagram of an embodiment of a device implementinga correlithm object core in a node for a correlithm object processingsystem;

FIG. 11 is an embodiment of a graph of probability distributions foradjacent root correlithm objects;

FIG. 12A is an embodiment of a string correlithm object generator;

FIG. 12B is an embodiment of a table demonstrating a change in bitvalues associated with sub-string correlithm objects;

FIG. 13 is an embodiment of a process for generating a string correlithmobject;

FIG. 14 is an embodiment of discrete data values mapped to sub-stringcorrelithm objects of a string correlithm object;

FIG. 15A is an embodiment of analog data values mapped to sub-stringcorrelithm objects of a string correlithm object;

FIG. 15B is an embodiment of a table demonstrating how to map analogdata values to sub-string correlithm objects using interpolation;

FIG. 16 is an embodiment of non-string correlithm objects mapped tosub-string correlithm objects of a string correlithm object;

FIG. 17 is an embodiment of a process for mapping non-string correlithmobjects to sub-string correlithm objects of a string correlithm object;

FIG. 18 is an embodiment of sub-string correlithm objects of a firststring correlithm object mapped to sub-string correlithm objects of asecond string correlithm objects;

FIG. 19 is an embodiment of a process for mapping sub-string correlithmobjects of a first string correlithm object to sub-string correlithmobjects of a second string correlithm objects;

FIGS. 20A-C illustrate different embodiments of a distance table that isused to compare time-varying signals represented by string correlithmobjects in n-dimensional space;

FIGS. 21A-D illustrate different embodiments of two-dimensional imagesthat are compared against each other using string correlithm objects;

FIG. 22 illustrates one embodiment of a string correlithm objectvelocity detector;

FIG. 23 illustrates one embodiment of a correlithm object processingsystem to emulate recording and playback;

FIG. 24 illustrates an embodiment of a process for emulating recordingand playback in a correlithm object processing system;

FIG. 25 illustrates an embodiment of a triangle lattice correlithmobject generator;

FIGS. 26A-C illustrate embodiments of bit value tables;

FIGS. 27A-D illustrate embodiments of lattice correlithm objects;

FIG. 28 illustrates one embodiment of a quadrilateral lattice correlithmobject generator;

FIGS. 29A-B illustrate embodiments of bit value tables;

FIGS. 30A-B illustrate embodiments of lattice correlithm objects;

FIG. 31A illustrates one embodiment of an irregular lattice correlithmobject generator;

FIG. 31B illustrates one embodiment of an irregular lattice correlithmobject;

FIG. 32A illustrates one embodiment of a bidirectional string correlithmobject generator;

FIG. 32B illustrates one embodiment of a bit value table;

FIG. 33A illustrates one embodiment of a bidirectional string correlithmobject;

FIG. 33B illustrates one embodiment of intersecting multiple stringcorrelithm objects;

FIG. 34 illustrates an embodiment of a process for generatingbidirectional string correlithm objects and intersecting multiple stringcorrelithm objects; and

FIG. 35 illustrates embodiments of link nodes.

DETAILED DESCRIPTION

FIGS. 1-5 describe various embodiments of how a correlithm objectprocessing system may be implemented or emulated in hardware, such as aspecial purpose computer. FIGS. 6-19 describe various embodiments of howa correlithm object processing system can generate and use stringcorrelithm objects to record and faithfully playback data values.

FIG. 1 is a schematic view of an embodiment of a user device 100implementing correlithm objects 104 in an n-dimensional space 102.Examples of user devices 100 include, but are not limited to, desktopcomputers, mobile phones, tablet computers, laptop computers, or otherspecial purpose computer platform. The user device 100 is configured toimplement or emulate a correlithm object processing system that usescategorical numbers to represent data samples as correlithm objects 104in a high-dimensional space 102, for example a high-dimensional binarycube. Additional information about the correlithm object processingsystem is described in FIG. 3. Additional information about configuringthe user device 100 to implement or emulate a correlithm objectprocessing system is described in FIG. 5.

Conventional computers rely on the numerical order of ordinal binaryintegers representing data to perform various operations such ascounting, sorting, indexing, and mathematical calculations. Even whenperforming operations that involve other number systems (e.g. floatingpoint), conventional computers still resort to using ordinal binaryintegers to perform any operations. Ordinal based number systems onlyprovide information about the sequence order of the numbers themselvesbased on their numeric values. Ordinal numbers do not provide anyinformation about any other types of relationships for the data beingrepresented by the numeric values, such as similarity. For example, whena conventional computer uses ordinal numbers to represent data samples(e.g. images or audio signals), different data samples are representedby different numeric values. The different numeric values do not provideany information about how similar or dissimilar one data sample is fromanother. In other words, conventional computers are only able to makebinary comparisons of data samples which only results in determiningwhether the data samples match or do not match. Unless there is an exactmatch in ordinal number values, conventional systems are unable to tellif a data sample matches or is similar to any other data samples. As aresult, conventional computers are unable to use ordinal numbers bythemselves for determining similarity between different data samples,and instead these computers rely on complex signal processingtechniques. Determining whether a data sample matches or is similar toother data samples is not a trivial task and poses several technicalchallenges for conventional computers. These technical challenges resultin complex processes that consume processing power which reduces thespeed and performance of the system.

In contrast to conventional systems, the user device 100 operates as aspecial purpose machine for implementing or emulating a correlithmobject processing system. Implementing or emulating a correlithm objectprocessing system improves the operation of the user device 100 byenabling the user device 100 to perform non-binary comparisons (i.e.match or no match) between different data samples. This enables the userdevice 100 to quantify a degree of similarity between different datasamples. This increases the flexibility of the user device 100 to workwith data samples having different data types and/or formats, and alsoincreases the speed and performance of the user device 100 whenperforming operations using data samples. These improvements and otherbenefits to the user device 100 are described in more detail below andthroughout the disclosure.

For example, the user device 100 employs the correlithm objectprocessing system to allow the user device 100 to compare data sampleseven when the input data sample does not exactly match any known orpreviously stored input values. Implementing a correlithm objectprocessing system fundamentally changes the user device 100 and thetraditional data processing paradigm. Implementing the correlithm objectprocessing system improves the operation of the user device 100 byenabling the user device 100 to perform non-binary comparisons of datasamples. In other words, the user device 100 is able to determine howsimilar the data samples are to each other even when the data samplesare not exact matches. In addition, the user device 100 is able toquantify how similar data samples are to one another. The ability todetermine how similar data samples are to each other is unique anddistinct from conventional computers that can only perform binarycomparisons to identify exact matches.

The user device's 100 ability to perform non-binary comparisons of datasamples also fundamentally changes traditional data searching paradigms.For example, conventional search engines rely on finding exact matchesor exact partial matches of search tokens to identify related datasamples. For instance, conventional text-based search engines arelimited to finding related data samples that have text that exactlymatches other data samples. These search engines only provide a binaryresult that identifies whether or not an exact match was found based onthe search token. Implementing the correlithm object processing systemimproves the operation of the user device 100 by enabling the userdevice 100 to identify related data samples based on how similar thesearch token is to other data sample. These improvements result inincreased flexibility and faster search time when using a correlithmobject processing system. The ability to identify similarities betweendata samples expands the capabilities of a search engine to include datasamples that may not have an exact match with a search token but arestill related and similar in some aspects. The user device 100 is alsoable to quantify how similar data samples are to each other based oncharacteristics besides exact matches to the search token. Implementingthe correlithm object processing system involves operating the userdevice 100 in an unconventional manner to achieve these technologicalimprovements as well as other benefits described below for the userdevice 100.

Computing devices typically rely on the ability to compare data sets(e.g. data samples) to one another for processing. For example, insecurity or authentication applications a computing device is configuredto compare an input of an unknown person to a data set of known people(or biometric information associated with these people). The problemsassociated with comparing data sets and identifying matches based on thecomparison are problems necessarily rooted in computer technologies. Asdescribed above, conventional systems are limited to a binary comparisonthat can only determine whether an exact match is found. As an example,an input data sample that is an image of a person may have differentlighting conditions than previously stored images. In this example,different lighting conditions can make images of the same person appeardifferent from each other. Conventional computers are unable todistinguish between two images of the same person with differentlighting conditions and two images of two different people withoutcomplicated signal processing. In both of these cases, conventionalcomputers can only determine that the images are different. This isbecause conventional computers rely on manipulating ordinal numbers forprocessing.

In contrast, the user device 100 uses an unconventional configurationthat uses correlithm objects to represent data samples. Using correlithmobjects to represent data samples fundamentally changes the operation ofthe user device 100 and how the device views data samples. Byimplementing a correlithm object processing system, the user device 100can determine the distance between the data samples and other known datasamples to determine whether the input data sample matches or is similarto the other known data samples, as explained in detail below. Unlikethe conventional computers described in the previous example, the userdevice 100 is able to distinguish between two images of the same personwith different lighting conditions and two images of two differentpeople by using correlithm objects 104. Correlithm objects allow theuser device 100 to determine whether there are any similarities betweendata samples, such as between two images that are different from eachother in some respects but similar in other respects. For example, theuser device 100 is able to determine that despite different lightingconditions, the same person is present in both images.

In addition, the user device 100 is able to determine a degree ofsimilarity that quantifies how similar different data samples are to oneanother. Implementing a correlithm object processing system in the userdevice 100 improves the operation of the user device 100 when comparingdata sets and identifying matches by allowing the user device 100 toperform non-binary comparisons between data sets and to quantify thesimilarity between different data samples. In addition, using acorrelithm object processing system results in increased flexibility andfaster search times when comparing data samples or data sets. Thus,implementing a correlithm object processing system in the user device100 provides a technical solution to a problem necessarily rooted incomputer technologies.

The ability to implement a correlithm object processing system providesa technical advantage by allowing the system to identify and comparedata samples regardless of whether an exact match has been previousobserved or stored. In other words, using the correlithm objectprocessing system the user device 100 is able to identify similar datasamples to an input data sample in the absence of an exact match. Thisfunctionality is unique and distinct from conventional computers thatcan only identify data samples with exact matches.

Examples of data samples include, but are not limited to, images, files,text, audio signals, biometric signals, electric signals, or any othersuitable type of data. A correlithm object 104 is a point in then-dimensional space 102, sometimes called an “n-space.” The value ofrepresents the number of dimensions of the space. For example, ann-dimensional space 102 may be a 3-dimensional space, a 50-dimensionalspace, a 100-dimensional space, or any other suitable dimension space.The number of dimensions depends on its ability to support certainstatistical tests, such as the distances between pairs of randomlychosen points in the space approximating a normal distribution. In someembodiments, increasing the number of dimensions in the n-dimensionalspace 102 modifies the statistical properties of the system to provideimproved results. Increasing the number of dimensions increases theprobability that a correlithm object 104 is similar to other adjacentcorrelithm objects 104. In other words, increasing the number ofdimensions increases the correlation between how close a pair ofcorrelithm objects 104 are to each other and how similar the correlithmobjects 104 are to each other.

Correlithm object processing systems use new types of data structurescalled correlithm objects 104 that improve the way a device operates,for example, by enabling the device to perform non-binary data setcomparisons and to quantify the similarity between different datasamples. Correlithm objects 104 are data structures designed to improvethe way a device stores, retrieves, and compares data samples in memory.Unlike conventional data structures, correlithm objects 104 are datastructures where objects can be expressed in a high-dimensional spacesuch that distance 106 between points in the space represent thesimilarity between different objects or data samples. In other words,the distance 106 between a pair of correlithm objects 104 in then-dimensional space 102 indicates how similar the correlithm objects 104are from each other and the data samples they represent. Correlithmobjects 104 that are close to each other are more similar to each otherthan correlithm objects 104 that are further apart from each other. Forexample, in a facial recognition application, correlithm objects 104used to represent images of different types of glasses may be relativelyclose to each other compared to correlithm objects 104 used to representimages of other features such as facial hair. An exact match between twodata samples occurs when their corresponding correlithm objects 104 arethe same or have no distance between them. When two data samples are notexact matches but are similar, the distance between their correlithmobjects 104 can be used to indicate their similarities. In other words,the distance 106 between correlithm objects 104 can be used to identifyboth data samples that exactly match each other as well as data samplesthat do not match but are similar. This feature is unique to acorrelithm processing system and is unlike conventional computers thatare unable to detect when data samples are different but similar in someaspects.

Correlithm objects 104 also provide a data structure that is independentof the data type and format of the data samples they represent.Correlithm objects 104 allow data samples to be directly comparedregardless of their original data type and/or format. In some instances,comparing data samples as correlithm objects 104 is computationally moreefficient and faster than comparing data samples in their originalformat. For example, comparing images using conventional data structuresinvolves significant amounts of image processing which is time consumingand consumes processing resources. Thus, using correlithm objects 104 torepresent data samples provides increased flexibility and improvedperformance compared to using other conventional data structures.

In one embodiment, correlithm objects 104 may be represented usingcategorical binary strings. The number of bits used to represent thecorrelithm object 104 corresponds with the number of dimensions of then-dimensional space 102 where the correlithm object 102 is located. Forexample, each correlithm object 104 may be uniquely identified using a64-bit string in a 64-dimensional space 102. As another example, eachcorrelithm object 104 may be uniquely identified using a 10-bit stringin a 10-dimensional space 102. In other examples, correlithm objects 104can be identified using any other suitable number of bits in a stringthat corresponds with the number of dimensions in the n-dimensionalspace 102.

In this configuration, the distance 106 between two correlithm objects104 can be determined based on the differences between the bits of thetwo correlithm objects 104. In other words, the distance 106 between twocorrelithm objects can be determined based on how many individual bitsdiffer between the correlithm objects 104. The distance 106 between twocorrelithm objects 104 can be computed using Hamming distance,anti-Hamming distance or any other suitable technique.

As an example, using a 10-dimensional space 102, a first correlithmobject 104 is represented by a first 10-bit string (1001011011) and asecond correlithm object 104 is represented by a second 10-bit string(1000011011). The Hamming distance corresponds with the number of bitsthat differ between the first correlithm object 104 and the secondcorrelithm object 104. Conversely, the anti-Hamming distance correspondswith the number of bits that are alike between the first correlithmobject 104 and the second correlithm object 104.Thus, the Hammingdistance between the first correlithm object 104 and the secondcorrelithm object 104 can be computed as follows:

$\quad\begin{matrix}1001011011 \\\underset{\_}{1000011011} \\0001000000\end{matrix}$

In this example, the Hamming distance is equal to one because only onebit differs between the first correlithm object 104 and the secondcorrelithm object. Conversely, the anti-Hamming distance is nine becausenine bits are the same between the first and second correlithm objects104. As another example, a third correlithm object 104 is represented bya third 10-bit string (0110100100). In this example, the Hammingdistance between the first correlithm object 104 and the thirdcorrelithm object 104 can be computed as follows:

$\quad\begin{matrix}1001011011 \\\underset{\_}{0110100100} \\1111111111\end{matrix}$

The Hamming distance is equal to ten because all of the bits aredifferent between the first correlithm object 104 and the thirdcorrelithm object 104. Conversely, the anti-Hamming distance is zerobecause none of the bits are the same between the first and thirdcorrelithm objects 104. In the previous example, a Hamming distanceequal to one indicates that the first correlithm object 104 and thesecond correlithm object 104 are close to each other in then-dimensional space 102, which means they are similar to each other.Similarly, an anti-Hamming distance equal to nine also indicates thatthe first and second correlithm objects are close to each other inn-dimensional space 102, which also means they are similar to eachother. In the second example, a Hamming distance equal to ten indicatesthat the first correlithm object 104 and the third correlithm object 104are further from each other in the n-dimensional space 102 and are lesssimilar to each other than the first correlithm object 104 and thesecond correlithm object 104. Similarly, an anti-Hamming distance equalto zero also indicates that that the first and third correlithm objects104 are further from each other in n-dimensional space 102 and are lesssimilar to each other than the first and second correlithm objects 104.In other words, the similarity between a pair of correlithm objects canbe readily determined based on the distance between the pair correlithmobjects, as represented by either Hamming distances or anti-Hammingdistances.

As another example, the distance between a pair of correlithm objects104 can be determined by performing an XOR operation between the pair ofcorrelithm objects 104 and counting the number of logical high values inthe binary string. The number of logical high values indicates thenumber of bits that are different between the pair of correlithm objects104 which also corresponds with the Hamming distance between the pair ofcorrelithm objects 104.

In another embodiment, the distance 106 between two correlithm objects104 can be determined using a Minkowski distance such as the Euclideanor “straight-line” distance between the correlithm objects 104. Forexample, the distance 106 between a pair of correlithm objects 104 maybe determined by calculating the square root of the sum of squares ofthe coordinate difference in each dimension.

The user device 100 is configured to implement or emulate a correlithmobject processing system that comprises one or more sensors 302, nodes304, and/or actors 306 in order to convert data samples betweenreal-world values or representations and to correlithm objects 104 in acorrelithm object domain. Sensors 302 are generally configured toconvert real-world data samples to the correlithm object domain. Nodes304 are generally configured to process or perform various operations oncorrelithm objects in the correlithm object domain. Actors 306 aregenerally configured to convert correlithm objects 104 into real-worldvalues or representations. Additional information about sensors 302,nodes 304, and actors 306 is described in FIG. 3.

Performing operations using correlithm objects 104 in a correlithmobject domain allows the user device 100 to identify relationshipsbetween data samples that cannot be identified using conventional dataprocessing systems. For example, in the correlithm object domain, theuser device 100 is able to identify not only data samples that exactlymatch an input data sample, but also other data samples that havesimilar characteristics or features as the input data samples.Conventional computers are unable to identify these types ofrelationships readily. Using correlithm objects 104 improves theoperation of the user device 100 by enabling the user device 100 toefficiently process data samples and identify relationships between datasamples without relying on signal processing techniques that require asignificant amount of processing resources. These benefits allow theuser device 100 to operate more efficiently than conventional computersby reducing the amount of processing power and resources that are neededto perform various operations.

FIG. 2 is a schematic view of an embodiment of a mapping betweencorrelithm objects 104 in different n-dimensional spaces 102. Whenimplementing a correlithm object processing system, the user device 100performs operations within the correlithm object domain using correlithmobjects 104 in different n-dimensional spaces 102. As an example, theuser device 100 may convert different types of data samples havingreal-world values into correlithm objects 104 in different n-dimensionalspaces 102. For instance, the user device 100 may convert data samplesof text into a first set of correlithm objects 104 in a firstn-dimensional space 102 and data samples of audio samples as a secondset of correlithm objects 104 in a second n-dimensional space 102.Conventional systems require data samples to be of the same type and/orformat to perform any kind of operation on the data samples. In someinstances, some types of data samples cannot be compared because thereis no common format available. For example, conventional computers areunable to compare data samples of images and data samples of audiosamples because there is no common format. In contrast, the user device100 implementing a correlithm object processing system is able tocompare and perform operations using correlithm objects 104 in thecorrelithm object domain regardless of the type or format of theoriginal data samples.

In FIG. 2, a first set of correlithm objects 104A are defined within afirst n-dimensional space 102A and a second set of correlithm objects104B are defined within a second n-dimensional space 102B. Then-dimensional spaces may have the same number of dimensions or adifferent number of dimensions. For example, the first n-dimensionalspace 102A and the second n-dimensional space 102B may both be threedimensional spaces. As another example, the first n-dimensional space102A may be a three-dimensional space and the second n-dimensional space102B may be a nine-dimensional space. Correlithm objects 104 in thefirst n-dimensional space 102A and second n-dimensional space 102B aremapped to each other. In other words, a correlithm object 104A in thefirst n-dimensional space 102A may reference or be linked with aparticular correlithm object 104B in the second n-dimensional space102B. The correlithm objects 104 may also be linked with and referencedwith other correlithm objects 104 in other n-dimensional spaces 102.

In one embodiment, a data structure such as table 200 may be used to mapor link correlithm objects 104 in different n-dimensional spaces 102. Insome instances, table 200 is referred to as a node table. Table 200 isgenerally configured to identify a first plurality of correlithm objects104 in a first n-dimensional space 102 and a second plurality ofcorrelithm objects 104 in a second n-dimensional space 102. Eachcorrelithm object 104 in the first n-dimensional space 102 is linkedwith a correlithm object 104 is the second n-dimensional space 102. Forexample, table 200 may be configured with a first column 202 that listscorrelithm objects 104A as source correlithm objects and a second column204 that lists corresponding correlithm objects 104B as targetcorrelithm objects. In other examples, table 200 may be configured inany other suitable manner or may be implemented using any other suitabledata structure. In some embodiments, one or more mapping functions maybe used to convert between a correlithm object 104 in a firstn-dimensional space and a correlithm object 104 is a secondn-dimensional space.

FIG. 3 is a schematic view of an embodiment of a correlithm objectprocessing system 300 that is implemented by a user device 100 toperform operations using correlithm objects 104. The system 300generally comprises a sensor 302, a node 304, and an actor 306. Thesystem 300 may be configured with any suitable number and/orconfiguration of sensors 302, nodes 304, and actors 306. An example ofthe system 300 in operation is described in FIG. 4. In one embodiment, asensor 302, a node 304, and an actor 306 may all be implemented on thesame device (e.g. user device 100). In other embodiments, a sensor 302,a node 304, and an actor 306 may each be implemented on differentdevices in signal communication with each other for example over anetwork. In other embodiments, different devices may be configured toimplement any combination of sensors 302, nodes 304, and actors 306.

Sensors 302 serve as interfaces that allow a user device 100 to convertreal-world data samples into correlithm objects 104 that can be used inthe correlithm object domain. Sensors 302 enable the user device 100 tocompare and perform operations using correlithm objects 104 regardlessof the data type or format of the original data sample. Sensors 302 areconfigured to receive a real-world value 320 representing a data sampleas an input, to determine a correlithm object 104 based on thereal-world value 320, and to output the correlithm object 104. Forexample, the sensor 302 may receive an image 301 of a person and outputa correlithm object 322 to the node 304 or actor 306. In one embodiment,sensors 302 are configured to use sensor tables 308 that link aplurality of real-world values with a plurality of correlithm objects104 in an n-dimensional space 102. Real-world values are any type ofsignal, value, or representation of data samples. Examples of real-worldvalues include, but are not limited to, images, pixel values, text,audio signals, electrical signals, and biometric signals. As an example,a sensor table 308 may be configured with a first column 312 that listsreal-world value entries corresponding with different images and asecond column 314 that lists corresponding correlithm objects 104 asinput correlithm objects. In other examples, sensor tables 308 may beconfigured in any other suitable manner or may be implemented using anyother suitable data structure. In some embodiments, one or more mappingfunctions may be used to translate between a real-world value 320 and acorrelithm object 104 in an n-dimensional space. Additional informationfor implementing or emulating a sensor 302 in hardware is described inFIG. 5.

Nodes 304 are configured to receive a correlithm object 104 (e.g. aninput correlithm object 104), to determine another correlithm object 104based on the received correlithm object 104, and to output theidentified correlithm object 104 (e.g. an output correlithm object 104).In one embodiment, nodes 304 are configured to use node tables 200 thatlink a plurality of correlithm objects 104 from a first n-dimensionalspace 102 with a plurality of correlithm objects 104 in a secondn-dimensional space 102. A node table 200 may be configured similar tothe table 200 described in FIG. 2. Additional information forimplementing or emulating a node 304 in hardware is described in FIG. 5.

Actors 306 serve as interfaces that allow a user device 100 to convertcorrelithm objects 104 in the correlithm object domain back toreal-world values or data samples. Actors 306 enable the user device 100to convert from correlithm objects 104 into any suitable type ofreal-world value. Actors 306 are configured to receive a correlithmobject 104 (e.g. an output correlithm object 104), to determine areal-world output value 326 based on the received correlithm object 104,and to output the real-world output value 326. The real-world outputvalue 326 may be a different data type or representation of the originaldata sample. As an example, the real-world input value 320 may be animage 301 of a person and the resulting real-world output value 326 maybe text 327 and/or an audio signal identifying the person. In oneembodiment, actors 306 are configured to use actor tables 310 that linka plurality of correlithm objects 104 in an n-dimensional space 102 witha plurality of real-world values. As an example, an actor table 310 maybe configured with a first column 316 that lists correlithm objects 104as output correlithm objects and a second column 318 that listsreal-world values. In other examples, actor tables 310 may be configuredin any other suitable manner or may be implemented using any othersuitable data structure. In some embodiments, one or more mappingfunctions may be employed to translate between a correlithm object 104in an n-dimensional space and a real-world output value 326. Additionalinformation for implementing or emulating an actor 306 in hardware isdescribed in FIG. 5.

A correlithm object processing system 300 uses a combination of a sensortable 308, a node table 200, and/or an actor table 310 to provide aspecific set of rules that improve computer-related technologies byenabling devices to compare and to determine the degree of similaritybetween different data samples regardless of the data type and/or formatof the data sample they represent. The ability to directly compare datasamples having different data types and/or formatting is a newfunctionality that cannot be performed using conventional computingsystems and data structures. Conventional systems require data samplesto be of the same type and/or format in order to perform any kind ofoperation on the data samples. In some instances, some types of datasamples are incompatible with each other and cannot be compared becausethere is no common format available. For example, conventional computersare unable to compare data samples of images with data samples of audiosamples because there is no common format available. In contrast, adevice implementing a correlithm object processing system uses acombination of a sensor table 308, a node table 200, and/or an actortable 310 to compare and perform operations using correlithm objects 104in the correlithm object domain regardless of the type or format of theoriginal data samples. The correlithm object processing system 300 usesa combination of a sensor table 308, a node table 200, and/or an actortable 310 as a specific set of rules that provides a particular solutionto dealing with different types of data samples and allows devices toperform operations on different types of data samples using correlithmobjects 104 in the correlithm object domain. In some instances,comparing data samples as correlithm objects 104 is computationally moreefficient and faster than comparing data samples in their originalformat. Thus, using correlithm objects 104 to represent data samplesprovides increased flexibility and improved performance compared tousing other conventional data structures. The specific set of rules usedby the correlithm object processing system 300 go beyond simply usingroutine and conventional activities in order to achieve this newfunctionality and performance improvements.

In addition, correlithm object processing system 300 uses a combinationof a sensor table 308, a node table 200, and/or an actor table 310 toprovide a particular manner for transforming data samples betweenordinal number representations and correlithm objects 104 in acorrelithm object domain. For example, the correlithm object processingsystem 300 may be configured to transform a representation of a datasample into a correlithm object 104, to perform various operations usingthe correlithm object 104 in the correlithm object domain, and totransform a resulting correlithm object 104 into another representationof a data sample. Transforming data samples between ordinal numberrepresentations and correlithm objects 104 involves fundamentallychanging the data type of data samples between an ordinal number systemand a categorical number system to achieve the previously describedbenefits of the correlithm object processing system 300.

FIG. 4 is a protocol diagram of an embodiment of a correlithm objectprocess flow 400. A user device 100 implements process flow 400 toemulate a correlithm object processing system 300 to perform operationsusing correlithm object 104 such as facial recognition. The user device100 implements process flow 400 to compare different data samples (e.g.images, voice signals, or text) to each other and to identify otherobjects based on the comparison. Process flow 400 provides instructionsthat allows user devices 100 to achieve the improved technical benefitsof a correlithm object processing system 300.

Conventional systems are configured to use ordinal numbers foridentifying different data samples. Ordinal based number systems onlyprovide information about the sequence order of numbers based on theirnumeric values, and do not provide any information about any other typesof relationships for the data samples being represented by the numericvalues such as similarity. In contrast, a user device 100 can implementor emulate the correlithm object processing system 300 which provides anunconventional solution that uses categorical numbers and correlithmobjects 104 to represent data samples. For example, the system 300 maybe configured to use binary integers as categorical numbers to generatecorrelithm objects 104 which enables the user device 100 to performoperations directly based on similarities between different datasamples. Categorical numbers provide information about how similardifferent data sample are from each other. Correlithm objects 104generated using categorical numbers can be used directly by the system300 for determining how similar different data samples are from eachother without relying on exact matches, having a common data type orformat, or conventional signal processing techniques.

A non-limiting example is provided to illustrate how the user device 100implements process flow 400 to emulate a correlithm object processingsystem 300 to perform facial recognition on an image to determine theidentity of the person in the image. In other examples, the user device100 may implement process flow 400 to emulate a correlithm objectprocessing system 300 to perform voice recognition, text recognition, orany other operation that compares different objects.

At step 402, a sensor 302 receives an input signal representing a datasample. For example, the sensor 302 receives an image of person's faceas a real-world input value 320. The input signal may be in any suitabledata type or format. In one embodiment, the sensor 302 may obtain theinput signal in real-time from a peripheral device (e.g. a camera). Inanother embodiment, the sensor 302 may obtain the input signal from amemory or database.

At step 404, the sensor 302 identifies a real-world value entry in asensor table 308 based on the input signal. In one embodiment, thesystem 300 identifies a real-world value entry in the sensor table 308that matches the input signal. For example, the real-world value entriesmay comprise previously stored images. The sensor 302 may compare thereceived image to the previously stored images to identify a real-worldvalue entry that matches the received image. In one embodiment, when thesensor 302 does not find an exact match, the sensor 302 finds areal-world value entry that closest matches the received image.

At step 406, the sensor 302 identifies and fetches an input correlithmobject 104 in the sensor table 308 linked with the real-world valueentry. At step 408, the sensor 302 sends the identified input correlithmobject 104 to the node 304. In one embodiment, the identified inputcorrelithm object 104 is represented in the sensor table 308 using acategorical binary integer string. The sensor 302 sends the binarystring representing to the identified input correlithm object 104 to thenode 304.

At step 410, the node 304 receives the input correlithm object 104 anddetermines distances 106 between the input correlithm object 104 andeach source correlithm object 104 in a node table 200. In oneembodiment, the distance 106 between two correlithm objects 104 can bedetermined based on the differences between the bits of the twocorrelithm objects 104. In other words, the distance 106 between twocorrelithm objects can be determined based on how many individual bitsdiffer between a pair of correlithm objects 104. The distance 106between two correlithm objects 104 can be computed using Hammingdistance or any other suitable technique. In another embodiment, thedistance 106 between two correlithm objects 104 can be determined usinga Minkowski distance such as the Euclidean or “straight-line” distancebetween the correlithm objects 104. For example, the distance 106between a pair of correlithm objects 104 may be determined bycalculating the square root of the sum of squares of the coordinatedifference in each dimension.

At step 412, the node 304 identifies a source correlithm object 104 fromthe node table 200 with the shortest distance 106. A source correlithmobject 104 with the shortest distance from the input correlithm object104 is a correlithm object 104 either matches or most closely matchesthe received input correlithm object 104.

At step 414, the node 304 identifies and fetches a target correlithmobject 104 in the node table 200 linked with the source correlithmobject 104. At step 416, the node 304 outputs the identified targetcorrelithm object 104 to the actor 306. In this example, the identifiedtarget correlithm object 104 is represented in the node table 200 usinga categorical binary integer string. The node 304 sends the binarystring representing to the identified target correlithm object 104 tothe actor 306.

At step 418, the actor 306 receives the target correlithm object 104 anddetermines distances between the target correlithm object 104 and eachoutput correlithm object 104 in an actor table 310. The actor 306 maycompute the distances between the target correlithm object 104 and eachoutput correlithm object 104 in an actor table 310 using a processsimilar to the process described in step 410.

At step 420, the actor 306 identifies an output correlithm object 104from the actor table 310 with the shortest distance 106. An outputcorrelithm object 104 with the shortest distance from the targetcorrelithm object 104 is a correlithm object 104 either matches or mostclosely matches the received target correlithm object 104.

At step 422, the actor 306 identifies and fetches a real-world outputvalue in the actor table 310 linked with the output correlithm object104. The real-world output value may be any suitable type of data samplethat corresponds with the original input signal. For example, thereal-world output value may be text that indicates the name of theperson in the image or some other identifier associated with the personin the image. As another example, the real-world output value may be anaudio signal or sample of the name of the person in the image. In otherexamples, the real-world output value may be any other suitablereal-world signal or value that corresponds with the original inputsignal. The real-world output value may be in any suitable data type orformat.

At step 424, the actor 306 outputs the identified real-world outputvalue. In one embodiment, the actor 306 may output the real-world outputvalue in real-time to a peripheral device (e.g. a display or a speaker).In one embodiment, the actor 306 may output the real-world output valueto a memory or database. In one embodiment, the real-world output valueis sent to another sensor 302. For example, the real-world output valuemay be sent to another sensor 302 as an input for another process.

FIG. 5 is a schematic diagram of an embodiment of a computerarchitecture 500 for emulating a correlithm object processing system 300in a user device 100. The computer architecture 500 comprises aprocessor 502, a memory 504, a network interface 506, and aninput-output (I/O) interface 508. The computer architecture 500 may beconfigured as shown or in any other suitable configuration.

The processor 502 comprises one or more processors operably coupled tothe memory 504. The processor 502 is any electronic circuitry including,but not limited to, state machines, one or more central processing unit(CPU) chips, logic units, cores (e.g. a multi-core processor),field-programmable gate array (FPGAs), application specific integratedcircuits (ASICs), graphics processing units (GPUs), or digital signalprocessors (DSPs). The processor 502 may be a programmable logic device,a microcontroller, a microprocessor, or any suitable combination of thepreceding. The processor 502 is communicatively coupled to and in signalcommunication with the memory 204. The one or more processors areconfigured to process data and may be implemented in hardware orsoftware. For example, the processor 502 may be 8-bit, 16-bit, 32-bit,64-bit or of any other suitable architecture. The processor 502 mayinclude an arithmetic logic unit (ALU) for performing arithmetic andlogic operations, processor registers that supply operands to the ALUand store the results of ALU operations, and a control unit that fetchesinstructions from memory and executes them by directing the coordinatedoperations of the ALU, registers and other components.

The one or more processors are configured to implement variousinstructions. For example, the one or more processors are configured toexecute instructions to implement sensor engines 510, node engines 512,actor engines 514, string correlithm object engine 522, and latticecorrelithm object engine 524. In an embodiment, the sensor engines 510,the node engines 512, the actor engines 514, the string correlithmobject engine 522, and the lattice correlithm object engine 524 areimplemented using logic units, FPGAs, ASICs, DSPs, or any other suitablehardware. The sensor engines 510, the node engines 512, the actorengines 514, the string correlithm object engine 522, and the latticecorrelithm object engine 524 are each configured to implement a specificset of rules or processes that provides an improved technologicalresult.

In one embodiment, the sensor engine 510 is configured implement sensors302 that receive a real-world value 320 as an input, determine acorrelithm object 104 based on the real-world value 320, and output thecorrelithm object 104. An example operation of a sensor 302 implementedby a sensor engine 510 is described in FIG. 4.

In one embodiment, the node engine 512 is configured to implement nodes304 that receive a correlithm object 104 (e.g. an input correlithmobject 104), determine another correlithm object 104 based on thereceived correlithm object 104, and output the identified correlithmobject 104 (e.g. an output correlithm object 104). A node 304implemented by a node engine 512 is also configured to compute distancesbetween pairs of correlithm objects 104. An example operation of a node304 implemented by a node engine 512 is described in FIG. 4.

In one embodiment, the actor engine 514 is configured to implementactors 306 that receive a correlithm object 104 (e.g. an outputcorrelithm object 104), determine a real-world output value 326 based onthe received correlithm object 104, and output the real-world outputvalue 326. An example operation of an actor 306 implemented by an actorengine 514 is described in FIG. 4.

In one embodiment, string correlithm object engine 522 is configured toimplement a string correlithm object generator 1200 and otherwiseprocess string correlithm objects 602 as described, for example, inconjunction with FIGS. 12-24; and a bidirectional string correlithmobject generator 3200 as described, for example, in conjunction withFIG. 32A. In one embodiment, lattice correlithm object engine 524 isconfigured to implement a lattice correlithm object generators 2500,2800, and 3100 as described, for example, in conjunction with FIGS. 25,28, and 31A, respectively.

The memory 504 comprises one or more non-transitory disks, tape drives,or solid-state drives, and may be used as an over-flow data storagedevice, to store programs when such programs are selected for execution,and to store instructions and data that are read during programexecution. The memory 504 may be volatile or non-volatile and maycomprise read-only memory (ROM), random-access memory (RAM), ternarycontent-addressable memory (TCAM), dynamic random-access memory (DRAM),and static random-access memory (SRAM). The memory 504 is operable tostore sensor instructions 516, node instructions 518, actor instructions520, sensor tables 308, node tables 200, actor tables 310, stringcorrelithm object tables 1220, 1400, 1500, 1520, 1600, and 1820, and/orany other data or instructions. Memory 504 further stores bit valuetables 2520 a-c, 2820 a-b, and 3220 as described, for example, inconjunction with FIGS. 26A-C, 29A-B, and 32B; and node tables 2513,2812, 3112, 3212, and 3500 a-c as described, for example, in conjunctionwith FIGS. 25, 28, 31A, 32A, and 35. The sensor instructions 516, thenode instructions 518, and the actor instructions 520 comprise anysuitable set of instructions, logic, rules, or code operable to executethe sensor engine 510, node engine 512, and the actor engine 514,respectively.

The sensor tables 308, the node tables 200, and the actor tables 310 maybe configured similar to the sensor tables 308, the node tables 200, andthe actor tables 310 described in FIG. 3, respectively.

The network interface 506 is configured to enable wired and/or wirelesscommunications. The network interface 506 is configured to communicatedata with any other device or system. For example, the network interface506 may be configured for communication with a modem, a switch, arouter, a bridge, a server, or a client. The processor 502 is configuredto send and receive data using the network interface 506.

The I/O interface 508 may comprise ports, transmitters, receivers,transceivers, or any other devices for transmitting and/or receivingdata with peripheral devices as would be appreciated by one of ordinaryskill in the art upon viewing this disclosure. For example, the I/Ointerface 508 may be configured to communicate data between theprocessor 502 and peripheral hardware such as a graphical userinterface, a display, a mouse, a keyboard, a key pad, and a touch sensor(e.g. a touch screen).

FIGS. 6 and 7 are schematic diagrams of an embodiment of a device 100implementing string correlithm objects 602 for a correlithm objectprocessing system 300. String correlithm objects 602 can be used by acorrelithm object processing system 300 to embed higher orders ofcorrelithm objects 104 within lower orders of correlithm objects 104.The order of a correlithm object 104 depends on the number of bits usedto represent the correlithm object 104. The order of a correlithm object104 also corresponds with the number of dimensions in the n-dimensionalspace 102 where the correlithm object 104 is located. For example, acorrelithm object 104 represented by a 64-bit string is a higher ordercorrelithm object 104 than a correlithm object 104 represented by 16-bitstring.

Conventional computing systems rely on accurate data input and areunable to detect or correct for data input errors in real time. Forexample, a conventional computing device assumes a data stream iscorrect even when the data stream has bit errors. When a bit erroroccurs that leads to an unknown data value, the conventional computingdevice is unable to resolve the error without manual intervention. Incontrast, string correlithm objects 602 enable a device 100 to performoperations such as error correction and interpolation within thecorrelithm object processing system 300. For example, higher ordercorrelithm objects 104 can be used to associate an input correlithmobject 104 with a lower order correlithm 104 when an input correlithmobject does not correspond with a particular correlithm object 104 in ann-dimensional space 102. The correlithm object processing system 300uses the embedded higher order correlithm objects 104 to definecorrelithm objects 104 between the lower order correlithm objects 104which allows the device 100 to identify a correlithm object 104 in thelower order correlithm objects n-dimensional space 102 that correspondswith the input correlithm object 104. Using string correlithm objects602, the correlithm object processing system 300 is able to interpolateand/or to compensate for errors (e.g. bit errors) which improve thefunctionality of the correlithm object processing system 300 and theoperation of the device 100.

In some instances, string correlithm objects 602 may be used torepresent a series of data samples or temporal data samples. Forexample, a string correlithm object 602 may be used to represent audioor video segments. In this example, media segments are represented bysequential correlithm objects that are linked together using a stringcorrelithm object 602.

FIG. 6 illustrates an embodiment of how a string correlithm object 602may be implemented within a node 304 by a device 100. In otherembodiments, string correlithm objects 602 may be integrated within asensor 302 or an actor 306. In 32-dimensional space 102 where correlithmobjects 104 can be represented by a 32-bit string, the 32-bit string canbe embedded and used to represent correlithm objects 104 in a lowerorder 3-dimensional space 102 which uses three bits. The 32-bit stringscan be partitioned into three 12-bit portions, where each portioncorresponds with one of the three bits in the 3-dimensional space 102.For example, the correlithm object 104 represented by the 3-bit binaryvalue of 000 may be represented by a 32-bit binary string of zeros andthe correlithm object represented by the binary value of 111 may berepresented by a 32-bit string of all ones. As another example, thecorrelithm object 104 represented by the 3-bit binary value of 100 maybe represented by a 32-bit binary string with 12 bits set to onefollowed by 24 bits set to zero. In other examples, string correlithmobjects 602 can be used to embed any other combination and/or number ofn-dimensional spaces 102.

In one embodiment, when a higher order n-dimensional space 102 isembedded in a lower order n-dimensional space 102, one or morecorrelithm objects 104 are present in both the lower order n-dimensionalspace 102 and the higher order n-dimensional space 102. Correlithmobjects 104 that are present in both the lower order n-dimensional space102 and the higher order n-dimensional space 102 may be referred to asparent correlithm objects 603. Correlithm objects 104 in the higherorder n-dimensional space 102 may be referred to as child correlithmobjects 604. In this example, the correlithm objects 104 in the3-dimensional space 102 may be referred to as parent correlithm objects603 while the correlithm objects 104 in the 32-dimensional space 102 maybe referred to as child correlithm objects 604. In general, childcorrelithm objects 604 are represented by a higher order binary stringthan parent correlithm objects 603. In other words, the bit strings usedto represent a child correlithm object 604 may have more bits than thebit strings used to represent a parent correlithm object 603. Thedistance between parent correlithm objects 603 may be referred to as astandard distance. The distance between child correlithm objects 604 andother child correlithm objects 604 or parent correlithm objects 603 maybe referred to as a fractional distance which is less than the standarddistance.

FIG. 7 illustrates another embodiment of how a string correlithm object602 may be implemented within a node 304 by a device 100. In otherembodiments, string correlithm objects 602 may be integrated within asensor 302 or an actor 306. In FIG. 7, a set of correlithm objects 104are shown within an n-dimensional space 102. In one embodiment, thecorrelithm objects 104 are equally spaced from adjacent correlithmobjects 104. A string correlithm object 602 comprises a parentcorrelithm object 603 linked with one or more child correlithm objects604. FIG. 7 illustrates three string correlithm objects 602 where eachstring correlithm object 602 comprises a parent correlithm object 603linked with six child correlithm objects 603. In other examples, then-dimensional space 102 may comprise any suitable number of correlithmobjects 104 and/or string correlithm objects 602.

A parent correlithm object 603 may be a member of one or more stringcorrelithm objects 602. For example, a parent correlithm object 603 maybe linked with one or more sets of child correlithm objects 604 in anode table 200. In one embodiment, a child correlithm object 604 mayonly be linked with one parent correlithm object 603. String correlithmobjects 602 may be configured to form a daisy chain or a linear chain ofchild correlithm objects 604. In one embodiment, string correlithmobjects 602 are configured such that child correlithm objects 604 do notform loops where the chain of child correlithm objects 604 intersectwith themselves. Each child correlithm objects 604 is less than thestandard distance away from its parent correlithm object 603. The childcorrelithm objects 604 are equally spaced from other adjacent childcorrelithm objects 604.

In one embodiment, a data structure such as node table 200 may be usedto map or link parent correlithm objects 603 with child correlithmobjects 604. The node table 200 is generally configured to identify aplurality of parent correlithm objects 603 and one or more childcorrelithm objects 604 linked with each of the parent correlithm objects603. For example, node table 200 may be configured with a first columnthat lists child correlithm objects 604 and a second column that listsparent correlithm objects 603. In other examples, the node table 200 maybe configured in any other suitable manner or may be implemented usingany other suitable data structure. In some embodiments, one or moremapping functions may be used to convert between a child correlithmobject 604 and a parent correlithm object 603.

FIG. 8 is a schematic diagram of another embodiment of a device 100implementing string correlithm objects 602 in a node 304 for acorrelithm object processing system 300. Previously in FIG. 7, a stringcorrelithm object 602 comprised of child correlithm objects 604 that areadjacent to a parent correlithm object 603. In FIG. 8, string correlithmobjects 602 comprise one or more child correlithm objects 604 in betweena pair of parent correlithm objects 603. In this configuration, thestring correlithm object 602 initially diverges from a first parentcorrelithm object 603A and then later converges toward a second parentcorrelithm object 603B. This configuration allows the correlithm objectprocessing system 300 to generate a string correlithm object 602 betweena particular pair of parent correlithm objects 603.

The string correlithm objects described in FIG. 8 allow the device 100to interpolate value between a specific pair of correlithm objects 104(i.e. parent correlithm objects 603). In other words, these types ofstring correlithm objects 602 allow the device 100 to performinterpolation between a set of parent correlithm objects 603.Interpolation between a set of parent correlithm objects 603 enables thedevice 100 to perform operations such as quantization which convertbetween different orders of correlithm objects 104.

In one embodiment, a data structure such as node table 200 may be usedto map or link the parent correlithm objects 603 with their respectivechild correlithm objects 604. For example, node table 200 may beconfigured with a first column that lists child correlithm objects 604and a second column that lists parent correlithm objects 603. In thisexample, a first portion of the child correlithm objects 604 is linkedwith the first parent correlithm object 603A and a second portion of thechild correlithm objects 604 is linked with the second parent correlithmobject 603B. In other examples, the node table 200 may be configured inany other suitable manner or may be implemented using any other suitabledata structure. In some embodiments, one or more mapping functions maybe used to convert between a child correlithm object 604 and a parentcorrelithm object 603.

FIG. 9 is an embodiment of a graph of a probability distribution 900 formatching a random correlithm object 104 with a particular correlithmobject 104. Axis 902 indicates the number of bits that are differentbetween a random correlithm object 104 with a particular correlithmobject 104. Axis 904 indicates the probability associated with aparticular number of bits being different between a random correlithmobject 104 and a particular correlithm object 104.

As an example, FIG. 9 illustrates the probability distribution 900 formatching correlithm objects 104 in a 64-dimensional space 102. In oneembodiment, the probability distribution 900 is approximately a Gaussiandistribution. As the number of dimensions in the n-dimensional space 102increases, the probability distribution 900 starts to shape more like animpulse response function. In other examples, the probabilitydistribution 900 may follow any other suitable type of distribution.

Location 906 illustrates an exact match between a random correlithmobject 104 with a particular correlithm object 104. As shown by theprobability distribution 900, the probability of an exact match betweena random correlithm object 104 with a particular correlithm object 104is extremely low. In other words, when an exact match occurs the eventis most likely deliberate and not a random occurrence.

Location 908 illustrates when all of the bits between the randomcorrelithm object 104 with the particular correlithm object 104 aredifferent. In this example, the random correlithm object 104 and theparticular correlithm object 104 have 64 bits that are different fromeach other. As shown by the probability distribution 900, theprobability of all the bits being different between the randomcorrelithm object 104 and the particular correlithm object 104 is alsoextremely low.

Location 910 illustrates an average number of bits that are differentbetween a random correlithm object 104 and the particular correlithmobject 104. In general, the average number of different bits between therandom correlithm object 104 and the particular correlithm object 104 isequal to

$\frac{n}{2}$

(also referred to as standard distance), where ‘n’ is the number ofdimensions in the n-dimensional space 102. In this example, the averagenumber of bits that are different between a random correlithm object 104and the particular correlithm object 104 is 32 bits.

Location 912 illustrates a cutoff region that defines a core distancefor a correlithm object core. The correlithm object 104 at location 906may also be referred to as a root correlithm object for a correlithmobject core. The core distance defines the maximum number of bits thatcan be different between a correlithm object 104 and the root correlithmobject to be considered within a correlithm object core for the rootcorrelithm object. In other words, the core distance defines the maximumnumber of hops away a correlithm object 104 can be from a rootcorrelithm object to be considered a part of the correlithm object corefor the root correlithm object. Additional information about acorrelithm object core is described in FIG. 10. In this example, thecutoff region defines a core distance equal to six standard deviationsaway from the average number of bits that are different between a randomcorrelithm object 104 and the particular correlithm object 104. Ingeneral, the standard deviation is equal to

$\sqrt{\frac{n}{4}},$

where ‘n’ is the number of dimensions in the n-dimensional space 102. Inthis example, the standard deviation of the 64-dimensional space 102 isequal to 4 bits. This means the cutoff region (location 912) is located24 bits away from location 910 which is 8 bits away from the rootcorrelithm object at location 906. In other words, the core distance isequal to 8 bits. This means that the cutoff region at location 912indicates that the core distance for a correlithm object core includescorrelithm objects 104 that have up to 8 bits different then the rootcorrelithm object or are up to 8 hops away from the root correlithmobject. In other examples, the cutoff region that defines the coredistance may be equal any other suitable value. For instance, the cutoffregion may be set to 2, 4, 8, 10, 12, or any other suitable number ofstandard deviations away from location 910.

FIG. 10 is a schematic diagram of an embodiment of a device 100implementing a correlithm object core 1002 in a node 304 for acorrelithm object processing system 300. In other embodiments,correlithm object cores 1002 may be integrated with a sensor 302 or anactor 306. Correlithm object cores 1002 can be used by a correlithmobject processing system 300 to classify or group correlithm objects 104and/or the data samples they represent. For example, a set of correlithmobjects 104 can be grouped together by linking them with a correlithmobject core 1402. The correlithm object core 1002 identifies the classor type associated with the set of correlithm objects 104.

In one embodiment, a correlithm object core 1002 comprises a rootcorrelithm object 1004 that is linked with a set of correlithm objects104. The set of correlithm objects 104 that are linked with the rootcorrelithm object 1004 are the correlithm objects 104 which are locatedwithin the core distance of the root correlithm object 1004. The set ofcorrelithm objects 104 are linked with only one root correlithm object1004. The core distance can be computed using a process similar to theprocess described in FIG. 9. For example, in a 64-dimensional space 102with a core distance defined at six sigma (i.e. six standarddeviations), the core distance is equal to 8-bits. This means thatcorrelithm objects 104 within up to eight hops away from the rootcorrelithm object 1004 are members of the correlithm object core 1002for the root correlithm object 1004.

In one embodiment, a data structure such as node table 200 may be usedto map or link root correlithm objects 1004 with sets of correlithmobjects 104. The node table 200 is generally configured to identify aplurality of root correlithm objects 1004 and correlithm objects 104linked with the root correlithm objects 1004. For example, node table200 may be configured with a first column that lists correlithm objectcores 1002, a second column that lists root correlithm objects 1004, anda third column that lists correlithm objects 104. In other examples, thenode table 200 may be configured in any other suitable manner or may beimplemented using any other suitable data structure. In someembodiments, one or more mapping functions may be used to convertbetween correlithm objects 104 and a root correlithm object 1004.

FIG. 11 is an embodiment of a graph of probability distributions 1100for adjacent root correlithm objects 1004. Axis 1102 indicates thedistance between the root correlithm objects 1004, for example, in unitsof bits. Axis 1104 indicates the probability associated with the numberof bits being different between a random correlithm object 104 and aroot correlithm object 1004.

As an example, FIG. 11 illustrates the probability distributions foradjacent root correlithm objects 1004 in a 1024-dimensional space 102.Location 1106 illustrates the location of a first root correlithm object1004 with respect to a second root correlithm object 1004. Location 1108illustrates the location of the second root correlithm object 1004. Eachroot correlithm object 1004 is located an average distance away fromeach other which is equal to

$\frac{n}{2},$

where ‘n’ is the number of dimensions in the n-dimensional space 102. Inthis example, the first root correlithm object 1004 and the second rootcorrelithm object 1004 are 512 bits or 32 standard deviations away fromeach other.

In this example, the cutoff region for each root correlithm object 1004is located at six standard deviations from locations 1106 and 1108. Inother examples, the cutoff region may be located at any other suitablelocation. For example, the cutoff region defining the core distance mayone, two, four, ten, or any other suitable number of standard deviationsaway from the average distance between correlithm objects 104 in then-dimensional space 102. Location 1110 illustrates a first cutoff regionthat defines a first core distance 1114 for the first root correlithmobject 1004. Location 1112 illustrates a second cutoff region thatdefines a second core distance 1116 for the second root correlithmobject 1004.

In this example, the core distances for the first root correlithm object1004 and the second root correlithm object 1004 do not overlap with eachother. This means that correlithm objects 104 within the correlithmobject core 1002 of one of the root correlithm objects 1004 are uniquelyassociated with the root correlithm object 1004 and there is noambiguity.

FIG. 12A illustrates one embodiment of a string correlithm objectgenerator 1200 configured to generate a string correlithm object 602 asoutput. String correlithm object generator 1200 is implemented by stringcorrelithm object engine 522 and comprises a first processing stage 1202a communicatively and logically coupled to a second processing stage1202 b. First processing stage 1202 receives an input 1204 and outputs afirst sub-string correlithm object 1206 a that comprises an n-bitdigital word wherein each bit has either a value of zero or one. In oneembodiment, first processing stage 1202 generates the values of each bitrandomly. Input 1204 comprises one or more parameters used to determinethe characteristics of the string correlithm object 602. For example,input 1204 may include a parameter for the number of dimensions, n, inthe n-dimensional space 102 (e.g., 64, 128, 256, etc.) in which togenerate the string correlithm object 602. Input 1204 may also include adistance parameter, δ, that indicates a particular number of bits of then-bit digital word (e.g., 4, 8, 16, etc.) that will be changed from onesub-string correlithm object 1206 to the next in the string correlithmobject 602. Second processing stage 1202 b receives the first sub-stringcorrelithm object 1206 a and, for each bit of the first sub-stringcorrelithm object 1206 a up to the particular number of bits identifiedin the distance parameter, δ, changes the value from a zero to a one orfrom a one to a zero to generate a second sub-string correlithm object1206 b. The bits of the first sub-string correlithm object 1206 a thatare changed in value for the second sub-string correlithm object 1206 bare selected randomly from the n-bit digital word. The other bits of then-bit digital word in second sub-string correlithm object 1206 b remainthe same values as the corresponding bits of the first sub-stringcorrelithm object 1206 a.

FIG. 12B illustrates a table 1220 that demonstrates the changes in bitvalues from a first sub-string correlithm object 1206 a to a secondsub-string correlithm object 1206 b. In this example, assume that n=64such that each sub-string correlithm object 1206 of the stringcorrelithm object 602 is a 64-bit digital word. As discussed previouslywith regard to FIG. 9, the standard deviation is equal to

$\sqrt{\frac{n}{4}},$

or four bits, for a 64-dimensional space 102. In one embodiment, thedistance parameter, δ, is selected to equal the standard deviation. Inthis embodiment, the distance parameter is also four bits which meansthat four bits will be changed from each sub-string correlithm object1206 to the next in the string correlithm object 602. In otherembodiments where it is desired to create a tighter correlation amongsub-string correlithm objects 1206, a distance parameter may be selectedto be less than the standard deviation (e.g., distance parameter ofthree bits or less where standard deviation is four bits). In stillother embodiments where it is desired to create a looser correlationamong sub-string correlithm objects 1206, a distance parameter may beselected to be more than the standard deviation (e.g., distanceparameter of five bits or more where standard deviation is four bits).Table 1220 illustrates the first sub-string correlithm object 1206 a inthe first column having four bit values that are changed, by secondprocessing stage 1202 b, from a zero to a one or from a one to a zero togenerate second sub-string correlithm object 1206 b in the secondcolumn. By changing four bit values, the core of the first sub-stringcorrelithm object 1206 a overlaps in 64-dimensional space with the coreof the second sub-string correlithm object 1206 b.

Referring back to FIG. 12A, the second processing stage 1202 b receivesfrom itself the second sub-string correlithm object 1206 b as feedback.For each bit of the second sub-string correlithm object 1206 b up to theparticular number of bits identified by the distance parameter, thesecond processing stage 1202 b changes the value from a zero to a one orfrom a one to a zero to generate a third sub-string correlithm object1206 c. The bits of the second sub-string correlithm object 1206 b thatare changed in value for the third sub-string correlithm object 1206 care selected randomly from the n-bit digital word. The other bits of then-bit digital word in third sub-string correlithm object 1206 c remainthe same values as the corresponding bits of the second sub-stringcorrelithm object 1206 b. Referring back to table 1220 illustrated inFIG. 12B, the second sub-string correlithm object 1206 b in the secondcolumn has four bit values that are changed, by second processing stage1202 b, from a zero to a one or from a one to a zero to generate thirdsub-string correlithm object 1206 c in the third column.

Referring back to FIG. 12A, the second processing stage 1202 bsuccessively outputs a subsequent sub-string correlithm object 1206 bychanging bit values of the immediately prior sub-string correlithmobject 1206 received as feedback, as described above. This processcontinues for a predetermined number of sub-string correlithm objects1206 in the string correlithm object 602. Together, the sub-stringcorrelithm objects 1206 form a string correlithm object 602 in which thefirst sub-string correlithm object 1206 a precedes and is adjacent tothe second sub-string correlithm object 1206 b, the second sub-stringcorrelithm object 1206 b precedes and is adjacent to the thirdsub-string correlithm object 1206 c, and so on. Each sub-stringcorrelithm object 1206 is separated from an adjacent sub-stringcorrelithm object 1206 in n-dimensional space 102 by a number of bitsrepresented by the distance parameter, δ.

FIG. 13 is a flowchart of an embodiment of a process 1300 for generatinga string correlithm object 602. At step 1302, a first sub-stringcorrelithm object 1206 a is generated, such as by a first processingstage 1202 a of a string correlithm object generator 1200. The firstsub-string correlithm object 1206 a comprises an n-bit digital word. Atstep 1304, a bit of the n-bit digital word of the sub-string correlithmobject 1206 is randomly selected and is changed at step 1306 from a zeroto a one or from a one to a zero. Execution proceeds to step 1308 whereit is determined whether to change additional bits in the n-bit digitalword. In general, process 1300 will change a particular number of bitsup to the distance parameter, δ. In one embodiment, as described abovewith regard to FIGS. 12A-B, the distance parameter is four bits. Ifadditional bits remain to be changed in the sub-string correlithm object1206, then execution returns to step 1304. If all of the bits up to theparticular number of bits in the distance parameter have already beenchanged, as determined at step 1308, then execution proceeds to step1310 where the second sub-string correlithm object 1206 b is output. Theother bits of the n-bit digital word in second sub-string correlithmobject 1206 b remain the same values as the corresponding bits of thefirst sub-string correlithm object 1206 a.

Execution proceeds to step 1312 where it is determined whether togenerate additional sub-string correlithm objects 1206 in the stringcorrelithm object 602. If so, execution returns back to step 1304 andthe remainder of the process occurs again to change particular bits upto the number of bits in the distance parameter, δ. Each subsequentsub-string correlithm object 1206 is separated from the immediatelypreceding sub-string correlithm object 1206 in n-dimensional space 102by a number of bits represented by the distance parameter, δ. If no moresub-string correlithm objects 1206 are to be generated in the stringcorrelithm object 602, as determined at step 1312, execution of process1300 terminates at steps 1314.

A string correlithm object 602 comprising a series of adjacentsub-string correlithm objects 1206 whose cores overlap with each otherpermits data values to be correlated with each other in n-dimensionalspace 102. Thus, where discrete data values have a pre-existingrelationship with each other in the real-world, those relationships canbe maintained in n-dimensional space 102 if they are represented bysub-string correlithm objects of a string correlithm object 602. Forexample, the letters of an alphabet have a relationship with each otherin the real-world. In particular, the letter “A” precedes the letters“B” and “C” but is closer to the letter “B” than the letter “C”. Thus,if the letters of an alphabet are to be represented by a stringcorrelithm object 602, the relationship between letter “A” and theletters “B” and “C” should be maintained such that “A” precedes but iscloser to letter “B” than letter “C.” Similarly, the letter “B” isequidistant to both letters “A” and “C,” but the letter “B” issubsequent to the letter “A” and preceding the letter “C”. Thus, if theletters of an alphabet are to be represented by a string correlithmobject 602, the relationship between letter “B” and the letters “A” and“C” should be maintained such that the letter “B” is equidistant butsubsequent to letter “A” and preceding letter “C.” The ability tomigrate these relationships between data values in the real-world torelationships among correlithm objects provides a significant advance inthe ability to record, store, and faithfully reproduce data withindifferent computing environments.

FIG. 14 illustrates how data values that have pre-existing relationshipswith each other can be mapped to sub-string correlithm objects 1206 of astring correlithm object 602 in n-dimensional space 102 by stringcorrelithm object engine 522 to maintain their relationships to eachother. Although the following description of FIG. 14 is illustrated withrespect to letters of an alphabet as representing data values that havepre-existing relationships to each other, other data values can also bemapped to string correlithm objects 602 using the techniques discussedherein. In particular, FIG. 14 illustrates a node table 1400 stored inmemory 504 that includes a column for a subset of sub-string correlithmobjects 1206 of a string correlithm object 602. The first sub-stringcorrelithm object 1206 a is mapped to a discrete data value, such as theletter “A” of the alphabet. The second sub-string correlithm object 1206b is mapped to a discrete data value, such as the letter “B” of thealphabet, and so on with sub-string correlithm objects 1206 c and 1206 dmapped to the letters “C” and “D”. As discussed above, the letters ofthe alphabet have a correlation with each other, including a sequence,an ordering, and a distance from each other. These correlations amongletters of the alphabet could not be maintained as represented inn-dimensional space if each letter was simply mapped to a randomcorrelithm object 104. Accordingly, to maintain these correlations, theletters of the alphabet are mapped to sub-string correlation objects1206 of a string correlation object 602. This is because, as describedabove, the adjacent sub-string correlation objects 1206 of a stringcorrelation object 602 also have a sequence, an ordering, and a distancefrom each other that can be maintained in n-dimensional space.

In particular, just like the letters “A,” “B,” “C,” and “D” have anordered sequence in the real-world, the sub-string correlithm objects1206 a, 1206 b, 1206 c, and 1206 d have an ordered sequence and distancerelationships to each other in n-dimensional space. Similarly, just likethe letter “A” precedes but is closer to the letter “B” than the letter“C” in the real-world, so too does the sub-string correlithm object 1206a precede but is closer to the sub-string correlithm object 1206 b thanthe sub-string correlithm object 1206 c in n-dimensional space.Similarly, just like the letter “B” is equidistant to but in between theletters “A” and “C” in the real world, so too is the sub-stringcorrelithm object 1206 b equidistant to but in between the sub-stringcorrelithm objects 1206 a and 1206 c in n-dimensional space. Althoughthe letters of the alphabet are used to provide an example of data inthe real world that has a sequence, an ordering, and a distancerelationship to each other, one of skill in the art will appreciate thatany data with those characteristics in the real world can be representedby sub-string correlithm objects 1206 to maintain those relationships inn-dimensional space.

Because the sub-string correlithm objects 1206 of a string correlithmobject 602 maintains the sequence, ordering, and/or distancerelationships between real-world data in n-dimensional space, node 304can output the real-world data values (e.g., letters of the alphabet) inthe sequence in which they occurred. In one embodiment, the sub-stringcorrelithm objects 1206 can also be associated with timestamps, t₁₋₄, toaid with maintaining the relationship of the real-world data with asequence using the time at which they occurred. For example, sub-stringcorrelithm object 1206 a can be associated with a first timestamp, t₁;sub-string correlithm object 1206 b can be associated with a secondtimestamp, t₂; and so on. In one embodiment where the real-world datarepresents frames of a video signal that occur at different times of anordered sequence, maintaining a timestamp in the node table 1400 aidswith the faithful reproduction of the real-world data at the correcttime in the ordered sequence. In this way, the node table 1400 can actas a recorder by recording discrete data values for a time periodextending from at least the first timestamp, t₁ to a later timestamp,t_(n) 6. Also, in this way, the node 304 is also configured to reproduceor playback the real-world data represented by the sub-string correlithmobjects 1206 in the node table 1400 for a period of time extending fromat least the first timestamp, ti to a later timestamp, t_(n). Theability to record real-world data, associate it to sub-string correlithmobjects 1206 in n-dimensional space while maintaining its order,sequence, and distance relationships, and subsequently faithfullyreproduce the real-world data as originally recorded provides asignificant technical advantage to computing systems.

The examples described above relate to representing discrete datavalues, such as letters of an alphabet, using sub-string correlithmobjects 1206 of a string correlithm object 602. However, sub-stringcorrelithm objects 1206 also provide the flexibility to representnon-discrete data values, or analog data values, using interpolationfrom the real world to n-dimensional space 102. FIG. 15A illustrates howanalog data values that have pre-existing relationships with each othercan be mapped to sub-string correlithm objects 1206 of a stringcorrelithm object 602 in n-dimensional space 102 by string correlithmobject engine 522 to maintain their relationships to each other. FIG.15A illustrates a node table 1500 stored in memory 504 that includes acolumn for each sub-string correlithm object 1206 of a string correlithmobject 602. The first sub-string correlithm object 1206 a is mapped toan analog data value, such as the number “1.0”. The second sub-stringcorrelithm object 1206 b is mapped to an analog data value, such as thenumber “2.0”, and so on with sub-string correlithm objects 1206 c and1206 d mapped to the numbers “3.0” and “4.0.” Just like the letters ofthe alphabet described above, these numbers have a correlation with eachother, including a sequence, an ordering, and a distance from eachother. One difference between representing discrete data values (e.g.,letters of an alphabet) and analog data values (e.g., numbers) usingsub-string correlithm objects 1206 is that new analog data values thatfall between pre-existing analog data values can be represented usingnew sub-string correlithm objects 1206 using interpolation, as describedin detail below.

If node 304 receives an input representing an analog data value of 1.5,for example, then string correlithm object engine 522 can determine anew sub-string correlithm object 1206 that maintains the relationshipbetween this input of 1.5 and the other numbers that are alreadyrepresented by sub-string correlithm objects 1206. In particular, nodetable 1500 illustrates that the analog data value 1.0 is represented bysub-string correlithm object 1206 a and analog data value 2.0 isrepresented by sub-string correlithm object 1206 b. Because the analogdata value 1.5 is between the data values of 1.0 and 2.0, then a newsub-string correlithm object 1206 would be created in n-dimensionalspace 102 between sub-string correlithm objects 1206 a and 1206 b. Thisis done by interpolating the distance in n-dimensional space 102 betweensub-string correlithm objects 1206 a and 1206 b that corresponds to thedistance between 1.0 and 2.0 where 1.5 resides and representing thatinterpolation using an appropriate n-bit digital word. In this example,the analog data value of 1.5 is halfway between the data values of 1.0and 2.0. Therefore, the sub-string correlithm object 1206 that isdetermined to represent the analog data value of 1.5 would be halfwaybetween the sub-string correlithm objects 1206 a and 1206 b inn-dimensional space 102. Generating a sub-string correlithm object 1206that is halfway between sub-string correlithm objects 1206 a and 1206 bin n-dimensional space 102 involves modifying bits of the n-bit digitalwords representing the sub-string correlithm objects 1206 a and 1206 b.This process is illustrated with respect to FIG. 15B.

FIG. 15B illustrates a table 1520 with a first column representing then-bit digital word of sub-string correlithm object 1206 a that is mappedin the node table 1500 to the data value 1.0; a second columnrepresenting the n-bit digital word of sub-string correlithm object 1206b that is mapped in the node table 1500 to the data value 2.0; and athird column representing the n-bit digital word of sub-stringcorrelithm object 1206 ab that is generated and associated with the datavalue 1.5. Table 1520 is stored in memory 504. As described above withregard to table 1220, the distance parameter, δ, between adjacentsub-string correlithm objects 1206 a and 1206 b was chosen, in oneembodiment, to be four bits. This means that for a 64-bit digital word,four bits have been changed from a zero to a one or from a one to a zeroin order to generate sub-string correlithm object 1206 b from sub-stringcorrelithm object 1206 a.

In order to generate sub-string correlithm object 1206 ab to representthe data value of 1.5, a particular subset of those four changed bitsbetween sub-string correlithm objects 1206 a and 1206 b should bemodified. Moreover, the actual bits that are changed should be selectedsuccessively from one end of the n-bit digital word or the other end ofthe n-bit digital word. Because the data value of 1.5 is exactly halfwaybetween the data values of 1.0 and 2.0, then it can be determined thatexactly half of the four bits that are different between sub-stringcorrelithm object 1206 a and sub-string correlithm object 1206 b shouldbe changed to generate sub-string correlithm object 1206 ab. In thisparticular example, therefore, starting from one end of the n-bitdigital word as indicated by arrow 1522, the first bit that was changedfrom a value of one in sub-string correlithm object 1206 a to a value ofzero in sub-string correlithm object 1206 b is changed back to a valueof one in sub-string correlithm object 1206 ab. Continuing from the sameend of the n-bit digital word as indicated by arrow 1522, the next bitthat was changed from a value of one in sub-string correlithm object1206 a to a value of zero in sub-string correlithm object 1206 b ischanged back to a value of one in sub-string correlithm object 1206 ab.The other two of the four bits that were changed from sub-stringcorrelithm object 1206 a to sub-string correlithm object 1206 b are notchanged back. Accordingly, two of the four bits that were differentbetween sub-string correlithm objects 1206 a and 1206 b are changed backto the bit values that were in sub-string correlithm object 1206 a inorder to generate sub-string correlithm object 1206 ab that is halfwaybetween sub-string correlithm objects 1206 a and 1206 b in n-dimensionalspace 102 just like data value 1.5 is halfway between data values 1.0and 2.0 in the real world.

Other input data values can also be interpolated and represented inn-dimensional space 102, as described above. For example, if the inputdata value received was 1.25, then it is determined to be one-quarter ofthe distance from the data value 1.0 and three-quarters of the distancefrom the data value 2.0. Accordingly, a sub-string correlithm object1206 ab can be generated by changing back three of the four bits thatdiffer between sub-string correlithm objects 1206 a and 1206 b. In thisregard, the sub-string correlithm object 1206 ab (which represents thedata value 1.25) will only differ by one bit from the sub-stringcorrelithm object 1206 a (which represents the data value 1.0) inn-dimensional space 102. Similarly, if the input data value received was1.75, then it is determined to be three-quarters of the distance fromthe data value 1.0 and one-quarter of the distance from the data value2.0. Accordingly, a sub-string correlithm object 1206 ab can begenerated by changing back one of the four bits that differ betweensub-string correlithm objects 1206 a and 1206 b. In this regard, thesub-string correlithm object 1206 ab (which represents the data value1.75) will differ by one bit from the sub-string correlithm object 1206b (which represents the data value 2.0) in n-dimensional space 102. Inthis way, the distance between data values in the real world can beinterpolated to the distance between sub-string correlithm objects 1206in n-dimensional space 102 in order to preserve the relationships amonganalog data values.

Although the example above was detailed with respect to changing bitvalues from the top end of the n-bit digital word represented by arrow1522, the bit values can also be successively changed from the bottomend of the n-bit digital word. The key is that of the bit values thatdiffer from sub-string correlithm object 1206 a to sub-string correlithmobject 1206 b, the bit values that are changed to generate sub-stringcorrelithm object 1206 ab should be taken consecutively as they areencountered whether from the top end of the n-bit digital word (asrepresented by arrow 1522) or from the bottom end of the n-bit digitalword. This ensures that sub-string correlithm object 1206 ab willactually be between sub-string correlithm objects 1206 a and 1206 brather than randomly drifting away from both of sub-string correlithmobjects 1206 a and 1206 b in n-dimensional space 102.

FIG. 16 illustrates how real-world data values can be aggregated andrepresented by correlithm objects 104 (also referred to as non-stringcorrelithm objects 104), which are then linked to correspondingsub-string correlithm objects 1206 of a string correlithm object 602 bystring correlithm object engine 522. As described above with regard toFIG. 12A, a string correlithm object generator 1200 generates sub-stringcorrelithm objects 1206 that are adjacent to each other in n-dimensionalspace 102 to form a string correlithm object 602. The sub-stringcorrelithm objects 1206 a-n embody an ordering, sequence, and distancerelationships to each other in n-dimensional space 102. As described indetail below, non-string correlithm objects 104 can be mapped tocorresponding sub-string correlithm objects 1206 and stored in a nodetable 1600 to provide an ordering or sequence among them inn-dimensional space 102. This allows node table 1600 to record, store,and faithfully reproduce or playback a sequence of events that arerepresented by non-string correlithm objects 104 a-n. In one embodiment,the sub-string correlithm objects 1206 and the non-string correlithmobjects 104 can both be represented by the same length of digital word,n, (e.g., 64 bit, 128 bit, 256 bit). In another embodiment, thesub-string correlithm objects 1206 can be represented by a digital wordof one length, n, and the non-string correlithm objects 104 can berepresented by a digital word of a different length, m.

In a particular embodiment, the non-string correlithm objects 104 a-ncan represent aggregated real-world data. For example, real-world datamay be generated related to the operation of an automated teller machine(ATM). In this example, the ATM machine may have a video camera and amicrophone to tape both the video and audio portions of the operation ofthe ATM by one or more customers in a vestibule of a bank facility ordrive-through. The ATM machine may also have a processor that conductsand stores information regarding any transactions between the ATM andthe customer associated with a particular account. The bank facility maysimultaneously record video, audio, and transactional aspects of theoperation of the ATM by the customer for security, audit, or otherpurposes. By aggregating the real-world data values into non-stringcorrelithm objects 104 and associating those non-string correlithmobjects 104 with sub-string correlithm objects 1206, as described ingreater detail below, the correlithm object processing system maymaintain the ordering, sequence, and other relationships between thereal-world data values in n-dimensional space 102 for subsequentreproduction or playback. Although the example above is detailed withrespect to three particular types of real-world data (i.e., video,audio, transactional data associated with a bank ATM) that areaggregated and represented by correlithm objects 104, it should beunderstood that any suitable number and combination of different typesof real-world data can be aggregated and represented in this example.

For a period of time from t₁ to t_(n), the ATM records video, audio, andtransactional real-world data. For example, the period of time mayrepresent an hour, a day, a week, a month, or other suitable time periodof recording. The real-world video data is represented by videocorrelithm objects 1602. The real-world audio data is represented byaudio correlithm objects 1604. The real-world transaction data isrepresented by transaction correlithm objects 1606. The correlithmobjects 1602, 1604, and 1606 can be aggregated to form non-stringcorrelithm objects 104. For example, at a first time, t₁, the ATMgenerates: (a) real-world video data that is represented as a firstvideo correlithm object 1602 a; (b) real-world audio data that isrepresented by a first audio correlithm object 1604 a; and (c)real-world transaction data that is represented by a first transactioncorrelithm object 1606 a. Correlithm objects 1602 a, 1604 a, and 1606 acan be represented as a single non-string correlithm object 104 a whichis then associated with first sub-string correlithm object 1206 a in thenode table 1600. In one embodiment, the timestamp, t₁, can also becaptured in the non-string correlithm object 104 a. In this way, threedifferent types of real-world data are captured, represented by anon-string correlithm object 104 and then associated with a portion ofthe string correlithm object 602.

Continuing with the example, at a second time, t₂, the ATM generates:(a) real-world video data that is represented as a second videocorrelithm object 1602 b; (b) real-world audio data that is representedby a second audio correlithm object 1604 b; and (c) real-worldtransaction data that is represented by a second transaction correlithmobject 1606 b. The second time, t₂, can be a predetermined time orsuitable time interval after the first time, t₁, or it can be at a timesubsequent to the first time, t₁, where it is determined that one ormore of the video, audio, or transaction data has changed in anmeaningful way (e.g., video data indicates that a new customer enteredthe vestibule of the bank facility; another audible voice is detected orthe customer has made an audible request to the ATM; or the customer isattempting a different transaction or a different part of the sametransaction). Correlithm objects 1602 b, 1604 b, and 1606 b can berepresented as a single non-string correlithm object 104 b which is thenassociated with second sub-string correlithm object 1206 b in the nodetable 1600. In one embodiment, the timestamp, t₂, can also be capturedin the non-string correlithm object 104 b.

Continuing with the example, at a third time, t₃, the ATM generates: (a)real-world video data that is represented as a third video correlithmobject 1602 c; (b) real-world audio data that is represented by a thirdaudio correlithm object 1604 c; and (c) real-world transaction data thatis represented by a third transaction correlithm object 1606 c. Thethird time, t₃, can be a predetermined time or suitable time intervalafter the second time, t₂, or it can be at a time subsequent to thesecond time, t₂, where it is determined that one or more of the video,audio, or transaction data has changed again in a meaningful way, asdescribed above. Correlithm objects 1602 c, 1604 c, and 1606 c can berepresented as a single non-string correlithm object 104 c which is thenassociated with third sub-string correlithm object 1206 c in the nodetable 1600. In one embodiment, the timestamp, t₃, can also be capturedin the non-string correlithm object 104 c.

Concluding with the example, at an n-th time, t_(n), the ATM generates:(a) real-world video data that is represented as an n-th videocorrelithm object 1602 n; (b) real-world audio data that is representedby an n-th audio correlithm object 1604 n; and (c) real-worldtransaction data that is represented by an n-th transaction correlithmobject 1606 n. The third time, t_(n), can be a predetermined time orsuitable time interval after a previous time, t_(n−1), or it can be at atime subsequent to the previous time, t_(n−1), where it is determinedthat one or more of the video, audio, or transaction data has changedagain in a meaningful way, as described above. Correlithm objects 1602n, 1604 n, and 1606 _(n) can be represented as a single non-stringcorrelithm object 104 n which is then associated with n-th sub-stringcorrelithm object 1206 _(n) in the node table 1600. In one embodiment,the timestamp, t_(n), can also be captured in the non-string correlithmobject 104 n.

As illustrated in FIG. 16, different types of real-world data (e.g.,video, audio, transactional) can be captured and represented bycorrelithm objects 1602, 1604, and 1606 at particular timestamps. Thosecorrelithm objects 1602, 1604, and 1606 can be aggregated intocorrelithm objects 104. In this way, the real-world data can be “fannedin” and represented by a common correlithm object 104. By capturingreal-world video, audio, and transaction data at different relevanttimestamps from t₁-t_(n), representing that data in correlithm objects104, and then associating those correlithm objects 104 with sub-stringcorrelithm objects 1206 of a string correlithm object 602, the nodetable 1600 described herein can store vast amounts of real-world data inn-dimensional space 102 for a period of time while preserving theordering, sequence, and relationships among real-world data events andcorresponding correlithm objects 104 so that they can be faithfullyreproduced or played back in the real-world, as desired. This provides asignificant savings in memory capacity.

FIG. 17 is a flowchart of an embodiment of a process 1700 for linkingnon-string correlithm objects 104 with sub-string correlithm objects1206. At step 1702, string correlithm object generator 1200 generates afirst sub-string correlithm object 1206 a. Execution proceeds to step1704 where correlithm objects 104 are used to represent different typesof real-world data at a first timestamp, t₁. For example, correlithmobject 1602 a represents real-world video data; correlithm object 1604 arepresents real-world audio data; and correlithm object 1606 arepresents real-world transaction data. At step 1706, each of correlithmobjects 1602 a, 1604 a, and 1606 a captured at the first timestamp, t₁,are aggregated and represented by a non-string correlithm object 104 a.Execution proceeds to step 1708, where non-string correlithm object 104a is linked to sub-string correlithm object 1206 a, and this associationis stored in node table 1600 at step 1710. At step 1712, it isdetermined whether real-world data at the next timestamp should becaptured. For example, if a predetermined time interval since the lasttimestamp has passed or if a meaningful change to the real-world datahas occurred since the last timestamp, then execution returns to steps1702-1710 where another sub-string correlithm object 1206 is generated(step 1702); correlithm objects representing real-world data is capturedat the next timestamp (step 1704); those correlithm objects areaggregated and represented in a non-string correlithm object 104 (step1706); that non-string correlithm object 104 is linked with a sub-stringcorrelithm object 1206 (step 1708); and this association is stored inthe node table 1600 (step 1710). If no further real-world data is to becaptured at the next timestamp, as determined at step 1712, thenexecution ends at step 1714.

FIG. 18 illustrates how sub-string correlithm objects 1206 a-e of afirst string correlithm object 602 a are linked to sub-string correlithmobjects 1206 x-z of a second string correlithm object 602 b by stringcorrelithm object engine 522. The first string correlithm object 602 aincludes sub-string correlithm objects 1206 a-e that are separated fromeach other by a first distance 1802 in n-dimensional space 102. Thesecond string correlithm object 602 b includes sub-string correlithmobjects 1206 x-z that are separated from each other by a second distance1804 in n-dimensional space 102. In one embodiment, the sub-stringcorrelithm objects 1206 a-e of the first string correlithm object 602 aand the sub-string correlithm objects 1206 x-z can both be representedby the same length of digital word, n, (e.g., 64-bit, 128-bit, 256-bit).In another embodiment, the sub-string correlithm objects 1206 a-e of thefirst string correlithm object 602 a can be represented by a digitalword of one length, n, and the sub-string correlithm objects 1206 x-z ofthe second string correlithm object 602 b can be represented by adigital word of a different length, m. Each sub-string correlithm object1206 a-e represents a particular data value, such as a particular typeof real-world data value. When a particular sub-string correlithm object1206 a-e of the first string correlithm object 602 is mapped to aparticular sub-string correlithm object 1206 x-z of the second stringcorrelithm object 602, as described below, then the data valueassociated with the sub-string correlithm object 1206 a-e of the firststring correlithm object 602 a becomes associated with the mappedsub-string correlithm object 1206 x-z of the second string correlithmobject 602 b.

Mapping data represented by sub-string correlithm objects 1206 a-e of afirst string correlithm object 602 a in a smaller n-dimensional space102 (e.g., 64-bit digital word) where the sub-string correlithm objects1206 a-e are more tightly correlated to sub-string correlithm objects1206 x-z of a second string correlithm object 602 b in a largern-dimensional space 102 (e.g., 256-bit digital word) where thesub-string correlithm objects 1206 x-y are more loosely correlated (orvice versa) can provide several technical advantages in a correlithmobject processing system. For example, such a mapping can be used tocompress data and thereby save memory resources. In another example,such a mapping can be used to spread out data and thereby createadditional space in n-dimensions for the interpolation of data. In yetanother example, such a mapping can be used to apply a transformationfunction to the data (e.g., linear transformation function or non-lineartransformation function) from the first string correlithm object 602 ato the second string correlithm object 602 b.

The mapping of a first string correlithm object 602 a to a secondcorrelithm object 602 b operates, as described below. First, a node 304receives a particular sub-string correlithm object 1206, such as 1206 billustrated in FIG. 18. To map this particular sub-string correlithmobject 1206 b to the second correlithm object 602 b, the node 304determines the proximity of it to corresponding sub-string correlithmobjects 1206 x and 1206 y in second string correlithm object 602 b(e.g., by determining the Hamming distance between 1206 b and 1206 x,and between 1206 b and 1206 y). In particular, node 304 determines afirst proximity 1806 in n-dimensional space between the sub-stringcorrelithm object 1206 b and sub-string correlithm object 1206 x; anddetermines a second proximity 1808 in n-dimensional space between thesub-string correlithm object 1206 b and sub-string correlithm object1206 y. As illustrated in FIG. 18, the first proximity 1806 is smallerthan the second proximity 1808. Therefore, sub-string correlithm object1206 b is closer in n-dimensional space 102 to sub-string correlithmobject 1206 x than to sub-string correlithm object 1206 y. Accordingly,node 304 maps sub-string correlithm object 1206 b of first stringcorrelithm object 602 a to sub-string correlithm object 1206 x of secondstring correlithm object 602 b and maps this association in node table1820 stored in memory 504.

The mapping of the first string correlithm object 602 a to a secondcorrelithm object 602 b continues in operation, as described below. Thenode 304 receives another particular sub-string correlithm object 1206,such as 1206 c illustrated in FIG. 18. To map this particular sub-stringcorrelithm object 1206 c to the second correlithm object 602 b, the node304 determines the proximity of it to corresponding sub-stringcorrelithm objects 1206 x and 1206 y in second string correlithm object602 b. In particular, node 304 determines a first proximity 1810 inn-dimensional space between the sub-string correlithm object 1206 c andsub-string correlithm object 1206 x; and determines a second proximity1812 in n-dimensional space between the sub-string correlithm object1206 c and sub-string correlithm object 1206 y. As illustrated in FIG.18, the second proximity 1812 is smaller than the second proximity 1810.Therefore, sub-string correlithm object 1206 c is closer inn-dimensional space 102 to sub-string correlithm object 1206 y than tosub-string correlithm object 1206 x. Accordingly, node 304 mapssub-string correlithm object 1206 c of first string correlithm object602 a to sub-string correlithm object 1206 y of second string correlithmobject 602 b and maps this association in node table 1820.

The sub-string correlithm objects 1206 a-e may be associated withtimestamps in order to capture a temporal relationship among them andwith the mapping to sub-string correlithm objects 1206 x-z. For example,sub-string correlithm object 1206 a may be associated with a firsttimestamp, second sub-string correlithm object 1206 b may be associatedwith a second timestamp later than the first timestamp, and so on.

FIG. 19 is a flowchart of an embodiment of a process 1900 for linking afirst string correlithm object 602 a with a second string correlithmobject 602 b. At step 1902, a first string correlithm object 602 a isreceived at node 304. The first correlithm object 602 a includes a firstplurality of sub-string correlithm objects 1206, such as 1206 a-eillustrated in FIG. 18. Each of these sub-string correlithm objects 1206a-e are separated from each other by a first distance 1802 inn-dimensional space 102. At step 1904, a second string correlithm object602 b is received at node 304. The second correlithm object 602 bincludes a second plurality of sub-string correlithm objects 1206, suchas 1206 x-z illustrated in FIG. 18. Each of these sub-string correlithmobjects 1206 x-z are separated from each other by a second distance 1804in n-dimensional space 102. At step 1906, node 304 receives a particularsub-string correlithm object 1206 of the first string correlithm object602 a. At step 1908, node 304 determines a first proximity inn-dimensional space 102, such as proximity 1806 illustrated in FIG. 18,to a corresponding sub-string correlithm object 1206 of secondcorrelithm object 602 b, such as sub-string correlithm object 1206 xillustrated in FIG. 18. At step 1910, node 304 determines a secondproximity in n-dimensional space 102, such as proximity 1808 illustratedin FIG. 18, to a corresponding sub-string correlithm object 1206 ofsecond correlithm object 602 b, such as sub-string correlithm object1206 y illustrated in FIG. 18.

At step 1912, node 304 selects the sub-string correlithm object 1206 ofsecond string correlithm object 602 b to which the particular sub-stringcorrelithm object 1206 received at step 1906 is closest in n-dimensionalspace based upon the first proximity determined at step 1908 and thesecond proximity determined at step 1910. For example, as illustrated inFIG. 18, sub-string correlithm object 1206 b is closer in n-dimensionalspace to sub-string correlithm object 1206 x than sub-string correlithmobject 1206 y based on first proximity 1806 being smaller than secondproximity 1808. Execution proceeds to step 1914 where node 304 maps theparticular sub-string correlithm object 1206 received at step 1906 tothe sub-string correlithm object 1206 of second string correlithm object602 b selected at step 1912. At step 1916, node 304 determines whetherthere are any additional sub-string correlithm objects 1206 of firststring correlithm object 602 a to map to the second string correlithmobject 602 b. If so, execution returns to perform steps 1906 through1914 with respect to a different particular sub-string correlithm object1206 of first string correlithm object 602 a. If not, executionterminates at step 1918.

Dynamic time warping (DTW) is a technique used to measure similaritybetween temporal sequences of data that may vary in speed. Dynamic timewarping can be applied to temporal sequences of video, audio, graphics,or any other form of data that can be turned into a linear sequence. Oneapplication for DTW is automatic speech recognition to cope withdifferent speaking speeds. For example, DTW can be used to find anoptimal match between two given time-varying sequences where thesequences are “warped” non-linearly in the time dimension to determine ameasure of their similarity independent of certain non-linear variationsin the time dimension. A technical problem associated with using dynamictime warping is the heavy computational burden required to processreal-world data using DTW algorithms. Examples of the computationalburden include significant delays in processing speeds and largeconsumption of memory. A technologically improved system is needed tocompare and determine matches between temporal sequences of data withoutthe computational burden of conventional forms of processing usingdynamic time warping algorithms. Using string correlithm objects 602 torepresent real-world data significantly reduces the computational burdenwith comparing time-varying sequences of data, such as voice signals. Asdescribed in detail above with respect to FIGS. 14-19, real-world datacan be mapped to string correlithm objects 602 and then compared to eachother in n-dimensional space 102 to find matches. This approachalleviates the significant computational burden associated with dynamictime warping algorithms. One reason for the reduction in computationalburden is because string correlithm objects 602 in n-dimensional space102 can represent vast amounts of real-world data such that the amountof memory used to store string correlithm objects 602 is orders ofmagnitude less than that which is required to store the correspondingreal-world data. Additionally, processors can compare string correlithmobjects 602 to each other in n-dimensional space 102 with fasterexecution and with less processing power than is required to compare thecorresponding real-world data.

FIG. 20A illustrates one embodiment of a distance table 2000 a stored inmemory 504 that is used to compare one time-varying signal representedby a first string correlithm object 602 aa in n-dimensional space (e.g.,64-bit, 128-bit, 256-bit, etc.), such as a first voice signal, withanother time-varying signal represented as a second string correlithmobject 602 bb in n-dimensional space (e.g., 64-bit, 128-bit, 256-bit,etc.), such as a second voice signal. Memory 504 also stores stringcorrelithm objects 602 aa and 602 bb. In one embodiment, the first voicesignal may be a template voice signal that is to be compared against aseries of sample voice signals stored in a database to find a match. Thetemplate voice signal may be a word or phrase uttered by a first personor entity that is being compared with a series of sample voice signalsuttered by the same or different person or entity in order to find amatch. Such a comparison and matching process may be useful in voice orspeech recognition systems, or in natural language processing systems.The first voice signal is mapped to string correlithm object 602 aausing one or more of the techniques described above, for example, withrespect to FIGS. 14-19. For example, the first voice signal includesdata values that are mapped to sub-string correlithm objects 1206 aa₁₋₉. In this embodiment, the second voice signal may be one of thesample voice signals to be compared against the first voice signal. Thesecond voice signal is mapped to string correlithm object 602 bb usingone or more of the techniques described above, for example, with respectto FIGS. 14-19. For example, the second voice signal includes datavalues that are mapped to sub-string correlithm objects 1206 bb ₁₋₉.

In operation, string correlithm object engine 522 of processor 502performs a comparison of string correlithm object 602 aa with stringcorrelithm object 602 bb in n-dimensional space 102, as described indetail below. In particular, engine 522 compares each sub-stringcorrelithm object 1206 aa ₁₋₉ pairwise against each sub-stringcorrelithm object 12066 bb ₁₋₉, and determines anti-Hamming distancesbased on this pairwise comparison. As described above with respect toFIG. 1, the anti-Hamming distance calculation can be used to determinethe similarity between sub-string correlithm objects 1206 of the firststring correlithm object 602 aa and the second string correlithm object602 bb. As described above with reference to FIG. 9, the average numberof different bits between a random correlithm object and a particularcorrelithm object is equal to

$\frac{n}{2}$

(also referred to as standard distance), where ‘n’ is the number ofdimensions in the n-dimensional space 102. The standard deviation isequal to

$\sqrt{\frac{n}{4}},$

where ‘n’ is the number of dimensions in the n-dimensional space 102.Thus, if a sub-string correlithm object 1206 aa of string correlithmobject 602 aa is statistically dissimilar to a corresponding sub-stringcorrelithm object 1206 bb of string correlithm object 602 bb, then theanti-Hamming distance is expected to be roughly equal to the standarddistance. Therefore, if the n-dimensional space 102 is 64-bits, then theanti-Hamming distance between two dissimilar correlithm objects isexpected to be roughly 32. If a sub-string correlithm object 12066 aa ofstring correlithm object 602 aa is statistically similar to acorresponding sub-string correlithm object 1206 bb of string correlithmobject 602 bb, then the anti-Hamming distance is expected to be roughlyequal to six standard deviations more than the standard distance.Therefore, if the n-dimensional space 102 is 64-bits, then theanti-Hamming distance between similar correlithm objects is expected tobe roughly 56 or more (i.e., 32 (standard distance)+24 (six standarddeviations)=56). In other embodiments, if the anti-Hamming distance isequal to four or five standard deviations beyond the standard distance,then the correlithm objects are determined to be statistically similar.

The use of six standard deviations away from the standard distance todetermine statistical similarity is also appropriate in a largern-dimensional space 102, such as an n-space of 256-bits. For example, in256-space, standard distance is 256/2, or 128. The standard deviation isthe square root of (256/4), or 8. So an exact match between twocorrelithm objects 602 in 256-space, represented by a Hamming distanceof 0 and an anti-Hamming distance of 256, is a statistical event that is128/8, or 16 standard deviations from the expected or standard distance.Similarly, a perfect “inverse match” between two correlithm objects 602in 256-space, represented by a Hamming distance of 256 and ananti-Hamming distance of 0, is also a statistical event that is(256-128)/8, or 16 standard deviations from the expected or standarddistance. Accordingly, as the n-dimensional space 102 grows larger, thenumber of standard deviations between the peak of the binomialdistribution and its end points also grows larger. However, in theseexamples of 256-space where an exact match lies 16 standard deviationsfrom the standard distance, statistical similarity may still be found 6standard deviations from the standard distance.

Engine 522 determines the anti-Hamming distances between sub-stringcorrelithm object 1206 aa ₁ of first string correlithm object 602 aa andeach of the sub-string correlithm objects 1206 bb ₁₋₉ of secondcorrelithm object 602 bb, and stores those values in a first column 2010of table 2000 a. Engine 522 determines the anti-Hamming distancesbetween sub-string correlithm object 1206 aa ₂ of first stringcorrelithm object 602 aa and each of the sub-string correlithm objects1206 bb ₁₋₉ of second correlithm object 602 bb, and stores those valuesin a second column 2012 of table 2000 a. Engine 522 repeats the pairwisedetermination of anti-Hamming distances between sub-string correlithmobjects 1206 aa ₃₋₉ of first string correlithm object 602 aa and each ofthe sub-string correlithm objects 1206 bb ₁₋₉ of second correlithmobject 602 bb, and stores those values in columns 2014-2026 of table2000 a, respectively.

The anti-Hamming distance values represented in the cells of table 2000a indicate which sub-string correlithm objects 1206 aa are statisticallysimilar or dissimilar to corresponding sub-string correlithm objects1206 bb. Cells of table 2000 a having an anti-Hamming distance value of64 in them, for example, indicate a similarity between sub-stringcorrelithm objects 1206 aa and 1206 bb. Cells of table 2000 a with a‘SD’ in them (for standard distance), for example, indicate adissimilarity between sub-string correlithm objects 1206 aa and 1206 bb.Although particular cells of table 2000 a have an anti-Hamming distancevalue of 64 in them, indicating a statistical similarity betweenparticular sub-string correlithm objects 1206 aa and 1206 bb, engine 522determines that there is no discernible pattern of similarity among anygroup of neighboring sub-string correlithm objects 1206 aa and 1206 bb.This indicates that while there may be some sporadic similaritiesbetween string correlithm object 602 aa and string correlithm object 602bb, no meaningful portions of them are statistically similar to eachother. Given that string correlithm object 602 aa represents a firstvoice signal and string correlithm object 602 bb represents a secondvoice signal in this embodiment, this means that no meaningful portionof those voice signals are statistically similar to each other. In otherwords, they are not a match. Although statistical similarity isrepresented in table 2000 a with an anti-Hamming distance value of 64 invarious cells to indicate a match among sub-string correlithm objects1206, it should be understood that a statistical similarity can bedetermined with an anti-Hamming distance that is anywhere from four tosix standard deviations greater than the standard distance.

FIG. 20B illustrates one embodiment of a distance table 2000 b stored inmemory 504 that is used to compare the first voice signal represented byfirst string correlithm object 602 aa in n-dimensional space (e.g.,64-bit, 128-bit, 256-bit, etc.), with another time-varying signalrepresented as a third string correlithm object 602 cc in n-dimensionalspace (e.g., 64-bit, 128-bit, 256-bit, etc.), such as a third voicesignal. In this embodiment, the third voice signal may be one of thesample voice signals to be compared against the first voice signal. Thethird voice signal is mapped to string correlithm object 602 cc usingone or more of the techniques described above, for example, with respectto FIGS. 14-19. For example, the third voice signal includes data valuesthat are mapped to sub-string correlithm objects 1206 cc ₁₋₉. Memory 504stores string correlithm object 602 cc.

In operation, string correlithm object engine 522 of processor 502performs a comparison of string correlithm object 602 aa with stringcorrelithm object 602 cc in n-dimensional space 102, as described indetail below. In particular, engine 522 compares each sub-stringcorrelithm object 1206 aa ₁₋₉ pairwise against each sub-stringcorrelithm object 12066 cc ₁₋₉.

Engine 522 determines the anti-Hamming distance between sub-stringcorrelithm object 1206 aa ₁ of first string correlithm object 602 aa andeach of the sub-string correlithm objects 1206 cc ₁₋₉ of thirdcorrelithm object 602 cc, and stores those values in a first column 2010of table 2000 b. Engine 522 determines the anti-Hamming distance betweensub-string correlithm object 1206 aa ₂ of first string correlithm object602 aa and each of the sub-string correlithm objects 1206 cc ₁₋₉ ofthird correlithm object 602 cc, and stores those values in a secondcolumn 2012 of table 2000 b. Engine 522 repeats the pairwisedetermination of anti-Hamming distances between sub-string correlithmobjects 1206 aa ₃₋₉ of first string correlithm object 602 aa and each ofthe sub-string correlithm objects 1206 cc ₃₋₉ of third correlithm object602 cc, and stores those values in columns 2014-2026 of table 2000 b,respectively.

The anti-Hamming distance values represented in the cells of table 2000b indicate which sub-string correlithm objects 1206 aa are statisticallysimilar or dissimilar to corresponding sub-string correlithm objects1206 cc. Although many cells of table 2000 b have an anti-Hammingdistance value of 64 in them, indicating statistical similarity betweenparticular sub-string correlithm objects 1206 aa and 1206 cc, engine 522determines that there also exists a discernible pattern of similarityamong a group of five neighboring sub-string correlithm objects 1206 aa₃₋₇ and 1206 cc ₃₋₇b. This pattern is represented as a trace 2030 inFIG. 20B. This trace 2030 indicates that at least a portion of stringcorrelithm object 602 aa is statistically similar to at least a portionof string correlithm object 602 cc. The trace 2030 is seen in table 2000b as extending from a lower left cell to an upper right cell, where eachcell in the trace 2030 has an anti-Hamming distance value of 64 in it.Although statistical similarity is represented in table 2000 b with ananti-Hamming distance value of 64 in various cells to indicate a matchamong sub-string correlithm objects 1206, it should be understood that astatistical similarity can be determined with an anti-Hamming distancethat is anywhere from four to six standard deviations greater than thestandard distance.

Given that string correlithm object 602 aa represents a first voicesignal and string correlithm object 602 cc represents a third voicesignal, this means that there is a relevant portion of those voicesignals that are statistically similar to each other. In other words,there is a match between at least a portion of these respective voicesignals. Trace 2030 therefore identifies the location of a match betweenstring correlithm objects 602 aa and 602 cc (i.e., match between 1206 aa₃₋₇ and 1206 cc ₃₋₇), as well as the quality of that match. Once engine522 locates a trace 2030 in table 2000 b, engine 522 sums together theanti-Hamming distance calculations for the cells in the trace 2030 todetermine a composite anti-Hamming distance calculation 2032. In thisexample, the composite anti-Hamming distance calculation 2032 is 320.

FIG. 20C illustrates one embodiment of a distance table 2000 c stored inmemory 504 that is used to compare the first voice signal represented byfirst string correlithm object 602 aa in n-dimensional space (e.g.,64-bit, 128-bit, 256-bit, etc.), with another time-varying signalrepresented as a fourth string correlithm object 602 dd in n-dimensionalspace (e.g., 64-bit, 128-bit, 256-bit, etc.), such as a fourth voicesignal. In this embodiment, the fourth voice signal may be one of thesample voice signals to be compared against the first voice signal. Thefourth voice signal is mapped to string correlithm object 602 dd usingone or more of the techniques described above, for example, with respectto FIGS. 14-19. For example, the fourth voice signal includes datavalues that are mapped to sub-string correlithm objects 1206 dd ₁₋₉.Memory 504 stores fourth string correlithm object 602 dd.

In operation, string correlithm object engine 522 of processor 502performs a comparison of string correlithm object 602 aa with stringcorrelithm object 602 dd in n-dimensional space 102, as described indetail below. In particular, engine 522 compares each sub-stringcorrelithm object 1206 aa ₁₋₉ pairwise against each sub-stringcorrelithm object 12066 dd ₁₋₉.

Engine 522 determines the anti-Hamming distance between sub-stringcorrelithm object 1206 aa ₁ of first string correlithm object 602 aa andeach of the sub-string correlithm objects 1206 dd ₁₋₉ of fourthcorrelithm object 602 dd, and stores those values in a first column 2010of table 2000 c. Engine 522 determines the anti-Hamming distance betweensub-string correlithm object 1206 aa ₂ of first string correlithm object602 aa and each of the sub-string correlithm objects 1206 dd ₁₋₉ offourth correlithm object 602 dd, and stores those values in a secondcolumn 2012 of table 2000 c. Engine 522 repeats the pairwisedetermination of anti-Hamming distances between sub-string correlithmobjects 1206 aa ₃₋₉ of first string correlithm object 602 aa and each ofthe sub-string correlithm objects 1206 dd ₁₋₉ of fourth correlithmobject 602 dd, and stores those values in columns 2014-2026 of table2000 c, respectively. The anti-Hamming distance values represented inthe cells of table 2000 c indicate which sub-string correlithm objects1206 aa are statistically similar or dissimilar to correspondingsub-string correlithm objects 1206 dd.

Although numerous cells of table 2000 c have an anti-Hamming distance of64 in them, indicating statistical similarity between particularsub-string correlithm objects 1206 aa and 1206 dd, engine 522 determinesthat there exists a pattern of similarity among a group of nineneighboring sub-string correlithm objects 1206 aa ₁₋₉ and 1206 cc ₁₋₉.This pattern is represented as a trace 2040 in table 2000 c. This trace2040 indicates that a portion of string correlithm object 602 aa isstatistically similar to a corresponding portion of string correlithmobject 602 dd. The trace 2040 is seen in table 2000 c as extending fromthe lowermost left cell to the uppermost right cell, where each cell inthe trace 2040 has an anti-Hamming distance value of 64 in it. Althoughstatistical similarity is represented in table 2000 c with ananti-Hamming distance value of 64 in various cells to indicate a matchamong sub-string correlithm objects 1206, it should be understood that astatistical similarity can be determined with an anti-Hamming distancethat is anywhere from four to six standard deviations greater than thestandard distance.

Given that string correlithm object 602 aa represents a first voicesignal and string correlithm object 602 dd represents a fourth voicesignal, this means that there is a significant portion of those voicesignals that are statistically similar to each other. In other words,there is a close match between the entirety of these respective voicesignals. Trace 2040 therefore identifies the location of a match betweenstring correlithm objects 602 aa and 602 dd (i.e., match between 1206 aa₁₋₉ and 1206 dd ₁₋₉), as well as the quality of that match. Once engine522 locates a trace 2040 in table 2000 c, engine 522 sums together theanti-Hamming distance calculations for the cells in the trace 2040 todetermine a composite anti-Hamming distance calculation 2042. In thisexample, the composite anti-Hamming distance calculation 2042 is 576.

Upon locating trace 2030 in table 2000 b and trace 2040 in table 2000 c,but no trace in table 2000 a, engine 522 determines that each of stringcorrelithm objects 602 cc and 602 dd are closer matches to stringcorrelithm object 602 aa than string correlithm object 602 bb.Furthermore, engine 522 further determines which of string correlithmobjects 602 cc and 602 dd is the closest match to string correlithmobject 602 aa by determining which string correlithm object 602 cc or602 dd has the largest composite anti-Hamming distance calculation. Inthis example, because string correlithm object 602 dd has a largercomposite anti-Hamming distance calculation than string correlithmobject 602 cc (i.e., 576>320), engine 522 determines that stringcorrelithm object 602 dd is the closest match to string correlithmobject 602 aa. Based on this determination, engine 522 furtherdetermines that the fourth voice signal is the closest match to thefirst voice signal.

The principles described above with respect to FIGS. 20A-C can beextended from a single dimensional data object, such as a voice signal,to a multi-dimensional data object, such as a two-dimensionalphotograph. In a multi-dimensional implementation, engine 522 canimplement a facial recognition system by comparing the data comprising atwo-dimensional image of a face, as represented by string correlithmobjects 602 v and 602 h, with the data comprising sample two-dimensionalimages of faces, also as represented by string correlithm objects 602 vand 602 h.

FIG. 21A illustrates an embodiment of a two-dimensional image 2100comprising pixels represented by real-world data. In this embodiment,image 2100 is a picture of a woman. Engine 522 represents the real-worlddata of image 2100 as a horizontal string correlithm object 602 h ₁ anda vertical string correlithm object 602 v ₁ pursuant to the techniquesdescribed above with respect to FIGS. 14-19. In particular, engine 522represents vertical slices of the real-world data in image 2100 (asrepresented by arrow 2102) in corresponding sub-string correlithmobjects 1206 v ₁₁₋₁₉. For example, engine 522 represents a firstvertical slice of the real-world data in image 2100 in sub-stringcorrelithm object 1206 v ₁₁; a second vertical slice of the real-worlddata in image 2100 in sub-string correlithm object 1206 v ₁₂; and so onfrom sub-string correlithm object 1206 v ₁₃ through sub-stringcorrelithm object 1206 v ₁₉. Similarly, engine 522 represents horizontalslices of the real-world data in image 2100 (as represented by arrow2104) in corresponding sub-string correlithm objects 1206 h ₁₁₋₁₉. Forexample, engine 522 represents a first horizontal slice of thereal-world data in image 2100 in sub-string correlithm object 1206 h ₁₁;a second vertical slice of the real-world data in image 2100 insub-string correlithm object 1206 h ₁₂; and so on from sub-stringcorrelithm object 1206 h ₁₃ through sub-string correlithm object 1206 h₁₉. Memory 504 stores string correlithm objects 602 v ₁ and 602 h ₁.

FIGS. 21B-D illustrate embodiments of two-dimensional images to becompared against image 2100 to determine similarities or a match. Inparticular, FIG. 21B illustrates an embodiment of a two-dimensionalimage 2110 comprising pixels represented by real-world data. In thisembodiment, image 2110 is a picture of a man. Engine 522 represents thereal-world data of image 2110 as a horizontal string correlithm object602 h ₂ and a vertical string correlithm object 602 v ₂ pursuant to thetechniques described above with respect to FIGS. 14-19. In particular,engine 522 represents vertical slices of the real-world data in image2110 (as represented by arrow 2112) in corresponding sub-stringcorrelithm objects 1206 v ₂₁₋₂₉. For example, engine 522 represents afirst vertical slice of the real-world data in image 2110 in sub-stringcorrelithm object 1206 v ₂₁; a second vertical slice of the real-worlddata in image 2110 in sub-string correlithm object 1206 v ₂₂; and so onfrom sub-string correlithm object 1206 v ₂₃ through sub-stringcorrelithm object 1206 v ₂₉. Similarly, engine 522 represents horizontalslices of the real-world data in image 2110 (as represented by arrow2114) in corresponding sub-string correlithm objects 1206 h ₂₁₋₂₉. Forexample, engine 522 represents a first horizontal slice of thereal-world data in image 2110 in sub-string correlithm object 1206 h ₂₁;a second vertical slice of the real-world data in image 2110 insub-string correlithm object 1206 h ₂₂; and so on from sub-stringcorrelithm object 1206 h ₂₃ through sub-string correlithm object 1206 h₂₉. Memory 504 stores string correlithm objects 602 v ₂ and 602 h ₂.

FIG. 21C illustrates an embodiment of a two-dimensional image 2120comprising pixels represented by real-world data. In this embodiment,image 2120 is a picture of a woman wearing glasses. Engine 522represents the real-world data of image 2120 as a horizontal stringcorrelithm object 602 h ₃ and a vertical string correlithm object 602 v₃ pursuant to the techniques described above with respect to FIGS.14-19. In particular, engine 522 represents vertical slices of thereal-world data in image 2120 (as represented by arrow 2122) incorresponding sub-string correlithm objects 1206 v ₃₁₋₃₉. For example,engine 522 represents a first vertical slice of the real-world data inimage 2120 in sub-string correlithm object 1206 v ₃₁; a second verticalslice of the real-world data in image 2120 in sub-string correlithmobject 1206 v ₃₂; and so on from sub-string correlithm object 1206 v ₃₃through sub-string correlithm object 1206 v ₃₉. Similarly, engine 522represents horizontal slices of the real-world data in image 2120 (asrepresented by arrow 2124) in corresponding sub-string correlithmobjects 1206 h ₃₁₋₃₉. For example, engine 522 represents a firsthorizontal slice of the real-world data in image 2120 in sub-stringcorrelithm object 1206 h ₃₁; a second vertical slice of the real-worlddata in image 2120 in sub-string correlithm object 1206 h ₃₂; and so onfrom sub-string correlithm object 1206 h ₃₃ through sub-stringcorrelithm object 1206 h ₃₉. Memory 504 stores string correlithm objects602 v ₃ and 602 h ₃.

FIG. 21D illustrates an embodiment of a two-dimensional image 2130comprising pixels represented by real-world data. In this embodiment,image 2130 is a picture of a young man. Engine 522 represents thereal-world data of image 2130 as a horizontal string correlithm object602 h ₄ and a vertical string correlithm object 602 v ₄ pursuant to thetechniques described above with respect to FIGS. 14-19. In particular,engine 522 represents vertical slices of the real-world data in image2130 (as represented by arrow 2132) in corresponding sub-stringcorrelithm objects 1206 v ₄₁₋₄₉. For example, engine 522 represents afirst vertical slice of the real-world data in image 2130 in sub-stringcorrelithm object 1206 v ₄₁; a second vertical slice of the real-worlddata in image 2130 in sub-string correlithm object 1206 v ₄₂; and so onfrom sub-string correlithm object 1206 v ₄₃ through sub-stringcorrelithm object 1206 v ₄₉. Similarly, engine 522 represents horizontalslices of the real-world data in image 2130 (as represented by arrow2134) in corresponding sub-string correlithm objects 1206 h ₄₁₋₄₉. Forexample, engine 522 represents a first horizontal slice of thereal-world data in image 2130 in sub-string correlithm object 1206 h ₄₁;a second vertical slice of the real-world data in image 2130 insub-string correlithm object 1206 h ₄₂; and so on from sub-stringcorrelithm object 1206 h ₄₃ through sub-string correlithm object 1206 h₄₉. Memory 504 stores string correlithm objects 602 v ₄ and 602 h ₄.

In operation, engine 522 compares each of the images 2110, 2120, and2130 against image 2100 in n-dimensional space 102 to determine the bestmatch. It does so by performing a comparison of string correlithm object602 v ₁ with each of string correlithm objects 602 v ₂₋₄ inn-dimensional space 102; and by performing a comparison of stringcorrelithm objects 602 h ₁ with each of string correlithm objects 602 h₂₋₄ in n-dimensional space 102. Engine 522 performs each of thehorizontal and vertical comparisons in n-dimensional space 102 bycalculating the anti-Hamming distances between corresponding pairs ofsub-string correlithm objects 1206, using the methodology describedabove with respect to FIGS. 20A-B. The following description of thisoperation is not described with respect to specific distance tables toavoid confusion, but it should be understood that the operationdescribed below uses distance tables to store the anti-Hamming distancevalues calculated by engine 522 when performing a pairwise comparison ofsub-string correlithm objects 1206, as described above with respect toFIGS. 20A-C.

Comparison of Image 2100 with Image 2110 in n-Dimensional Space

To compare image 2110 against image 2100 in n-dimensional space 102,engine 522 compares each sub-string correlithm object 1206 v ₁₁₋₁₉pairwise against each sub-string correlithm object 1206 v ₂₁₋₂₉ todetermine a plurality of anti-Hamming distances and stores them in afirst distance table, as described above with regard to FIGS. 20A-B.Engine 522 determines whether there is any discernible pattern ofsimilarity in the first distance table among any group of neighboringsub-string correlithm objects 1206 v ₁₁₋₁₉ and 1206 v ₂₁₋₂₉, asrepresented by the calculated anti-Hamming distances, as described abovewith regard to FIGS. 20A-B. If such a pattern of similarity is found(e.g., a trace), then engine 522 calculates a vertical compositeanti-Hamming distance 2116 by summing the anti-Hamming distance valueswithin the pattern.

Engine 522 also compares each sub-string correlithm object 1206 h ₁₁₋₁₉pairwise against each sub-string correlithm object 1206 h ₂₁₋₂₉ todetermine a plurality of anti-Hamming distances and stores them in asecond distance table, as described above with regard to FIGS. 20A-B.Engine 522 determines whether there is any discernible pattern ofsimilarity in the second distance table among any group of neighboringsub-string correlithm objects 1206 h ₁₁₋₁₉ and 1206 h ₂₁₋₂₉, asrepresented by the calculated anti-Hamming distances, as described abovewith regard to FIGS. 20A-B. If such a pattern of similarity is found(e.g., a trace), then engine 522 calculates a horizontal compositeanti-Hamming distance 2118 by summing the anti-Hamming distance valueswithin the pattern.

Engine 522 determines a composite anti-Hamming distance 2119 by summingthe vertical composite anti-Hamming distance 2116 and the horizontalcomposite anti-Hamming distance 2118. Engine 522 determines whether theimage 2110 is statistically similar to, or a match of, image 2100 basedon the magnitude of the calculated composite anti-Hamming distance 2119in comparison to a threshold. For example, in one embodiment, acomposite anti-Hamming distance 2119 that is roughly four standarddeviations greater than a standard distance composite value for then-dimensional string correlithm object 602 is considered to bestatistically similar.

Comparison of Image 2100 with Image 2120 in n-Dimensional Space

To compare image 2120 against image 2100 in n-dimensional space 102,engine 522 compares each sub-string correlithm object 1206 v ₁₁₋₁₉pairwise against each sub-string correlithm object 1206 v ₃₁₋₃₉ todetermine a plurality of anti-Hamming distances and stores them in afirst distance table, as described above with regard to FIGS. 20A-B.Engine 522 determines whether there is any discernible pattern ofsimilarity in the first distance table among any group of neighboringsub-string correlithm objects 1206 v ₁₁₋₁₉ and 1206 v ₃₁₋₃₉, asrepresented by the calculated anti-Hamming distances, as described abovewith regard to FIGS. 20A-B. If such a pattern of similarity is found(e.g., a trace), then engine 522 calculates a vertical compositeanti-Hamming distance 2126 by summing the anti-Hamming distance valueswithin the pattern.

Engine 522 also compares each sub-string correlithm object 1206 h ₁₁₋₁₉pairwise against each sub-string correlithm object 1206 h ₃₁₋₃₉ todetermine a plurality of anti-Hamming distances and stores them in asecond distance table, as described above with regard to FIGS. 20A-B.Engine 522 determines whether there is any discernible pattern ofsimilarity in the second distance table among any group of neighboringsub-string correlithm objects 1206 h ₁₁₋₁₉ and 1206 h ₃₁₋₃₉, asrepresented by the calculated anti-Hamming distances, as described abovewith regard to FIGS. 20A-B. If such a pattern of similarity is found(e.g., a trace), then engine 522 calculates a horizontal compositeanti-Hamming distance 2128 by summing the anti-Hamming distance valueswithin the pattern.

Engine 522 determines a composite anti-Hamming distance 2129 by summingthe vertical composite anti-Hamming distance 2126 and the horizontalcomposite anti-Hamming distance 2128. Engine 522 determines whether theimage 2120 is statistically similar to, or a match of, image 2100 basedon the magnitude of the calculated composite anti-Hamming distance 2129in comparison to a threshold. For example, in one embodiment, acomposite anti-Hamming distance 2129 that is roughly four standarddeviations greater than a standard distance composite value for then-dimensional string correlithm object 602 is considered to bestatistically similar.

Comparison of Image 2100 with Image 2130 in n-Dimensional Space

To compare image 2130 against image 2100 in n-dimensional space 102,engine 522 compares each sub-string correlithm object 1206 v ₁₁₋₁₉pairwise against each sub-string correlithm object 1206 v ₄₁₋₄₉ todetermine a plurality of anti-Hamming distances and stores them in afirst distance table, as described above with regard to FIGS. 20A-B.Engine 522 determines whether there is any discernible pattern ofsimilarity in the first distance table among any group of neighboringsub-string correlithm objects 1206 v ₁₁₋₁₉ and 1206 v ₄₁₋₄₉, asrepresented by the calculated anti-Hamming distances, as described abovewith regard to FIGS. 20A-B. If such a pattern of similarity is found(e.g., a trace), then engine 522 calculates a vertical compositeanti-Hamming distance 2136 by summing the anti-Hamming distance valueswithin the pattern.

Engine 522 also compares each sub-string correlithm object 1206 h ₁₁₋₁₉pairwise against each sub-string correlithm object 1206 h ₄₁₋₄₉ todetermine a plurality of anti-Hamming distances and stores them in asecond distance table, as described above with regard to FIGS. 20A-B.Engine 522 determines whether there is any discernible pattern ofsimilarity in the second distance table among any group of neighboringsub-string correlithm objects 1206 h ₁₁₋₁₉ and 1206 h ₄₁₋₄₉, asrepresented by the calculated anti-Hamming distances, as described abovewith regard to FIGS. 20A-B. If such a pattern of similarity is found(e.g., a trace), then engine 522 calculates a horizontal compositeanti-Hamming distance 2128 by summing the anti-Hamming distance valueswithin the pattern.

Engine 522 determines a composite anti-Hamming distance 2139 by summingthe vertical composite anti-Hamming distance 2136 and the horizontalcomposite anti-Hamming distance 2138. Engine 522 determines whether theimage 2130 is statistically similar to, or a match of, image 2100 basedon the magnitude of the calculated composite anti-Hamming distance 2139in comparison to a threshold. For example, in one embodiment, acomposite anti-Hamming distance 2139 that is roughly four standarddeviations greater than a standard distance composite value for then-dimensional string correlithm object 602 is considered to bestatistically similar.

Once engine 522 performs the comparisons of images 2110, 2120, and 2130with image 2100 in n-dimensional space 102, as described above, itcompares the composite anti-Hamming distance calculations 2119, 2129,and 2139 to determine which image is the closest match to image 2100. Inparticular, the largest composite anti-Hamming distance among thecalculations 2119, 2129, and 2139 is determined to be the closest matchto image 2100. Using the example images described above with respect toFIGS. 21A-D, the image 2120 of the woman wearing glasses is most likelyto be the closest match to the image 2100 of the woman without theglasses, and therefore it is expected that composite anti-Hammingdistance calculation 2129 would be greater than the anti-Hammingdistance calculation 2119 associated with the image 2110 of the man, andgreater than the anti-Hamming distance calculation 2139 associated withthe image 2130 of the young man. In this way, engine 522 is able toperform a comparison of multi-dimensional data objects in n-dimensionalspace using string correlithm objects 602 h and 602 v instead of themore computationally burdensome approach using dynamic time warpingalgorithms.

FIG. 22 illustrates one embodiment of a string correlithm objectvelocity detector 2200 configured to generate a string correlithm object602 bb that represents a time measurement between performing dataprocesses associated with a string correlithm object 602 aa. Stringcorrelithm object velocity detector 2200 and its constituent componentscan be implemented by processor 502, one or more of the engines 510,512, 514, and 522, and other elements of computer architecture 500,described above with respect to FIG. 5. Detector 2200 comprises a sensor302 configured to access a string correlithm object 602 aa and togenerate string correlithm object 602 bb. String correlithm object 602aa includes sub-string correlithm objects 1206 aa, 1206 bb, 1206 cc, and1206 dd that each comprise an n-bit digital word representing data.Although string correlithm object 602 aa is described as including foursub-string correlithm objects, it may include any number and combinationof sub-string correlithm objects in other embodiments. The stringcorrelithm object engine 522 processes the data in string correlithmobject 602 aa, as appropriate, for any particular task being performed.For example, the data processing that may be performed by processor 502on the data represented by string correlithm object 602 aa may include asequence of operations that collect and/or manipulate data, such as theconversion of raw data into machine-readable form, the flow of datathrough processor 502 to other elements of a system, any formatting ortransformation of the data, and any other computable function.

The measurement of how much time it takes to perform any of the dataprocesses associated with the sub-string correlithm objects 1206 aa,1206 bb, 1206 cc, and 1206 dd is illustrated in FIG. 22. In particular,a first time measure, t₁, represents the time between performing thefirst data process associated with sub-string correlithm object 1206 aaand performing the second data process associated with sub-stringcorrelithm object 1206 bb. A second time measure, t₂, represents thetime between performing the second data process associated withsub-string correlithm object 1206 bb and performing the third dataprocess associated with sub-string correlithm object 1206 cc. A thirdtime measure, t₃, represents the time between performing the third dataprocess associated with sub-string correlithm object 1206 cc andperforming the fourth data process associated with sub-string correlithmobject 1206 dd.

In one embodiment, one or more of the time measures, t₁, t₂, and t₃comprises a measurement of time between the completion of one dataprocess (e.g., first data process associated with sub-string correlithmobject 1206 aa) and the beginning of the next data process (e.g., seconddata process associated with sub-string correlithm object 1206 bb). Inanother embodiment, one or more of the time measures, t₁, t₂, and t₃comprises a measurement of time between the completion of one dataprocess (e.g., first data process associated with sub-string correlithmobject 1206 aa) and the completion of the next data process (e.g.,second data process associated with sub-string correlithm object 1206bb). In still another embodiment, one or more of the time measures, t₁,t₂, and t₃ comprise a measurement of time between the beginning of onedata process (e.g., first data process associated with sub-stringcorrelithm object 1206 aa) and the beginning of the next data process(e.g., second data process associated with sub-string correlithm object1206 bb). Other embodiments may organize the measurements of timeaccording to the needs of any particular application.

Sensor 302 observes these time measurements t₁, t₂, and t₃ as real-worlddata and outputs a string correlithm object 602 bb to represent them ascorrelithm objects. In particular, sensor 302 observes time measurementt₁ as real-world data and outputs a sub-string correlithm object 1206 xxto represent t₁ as a correlithm object 104. Sensor 302 observes timemeasurement t₂ as real-world data and outputs a sub-string correlithmobject 1206 _(yy) to represent t₂ as a correlithm object 104. Sensor 302observes time measurement t₃ as real-world data and outputs a sub-stringcorrelithm object 1206 zz to represent t₃ as a correlithm object 104.The combination of sub-string correlithm objects 1206 xx, 1206 yy, and1206 zz form string correlithm object 602 bb. In this way, sensor 302 isan “internal” sensor in that it that monitors the real-world datacharacterizing correlithm objects 104 internal to the correlithm objectprocessing system. As described above, a sensor engine 510 implementssensors 302. The operation of a sensor 302 to convert real-world datainto correlithm objects is described at length above, at least withrespect to FIGS. 3 and 4.

As described above, the sub-string correlithm objects 1206 aa, 1206 bb,1206 cc, and 1206 dd each comprise an n-bit digital word representingdata and/or a data process. The sub-string correlithm objects 1206 xx-zzeach comprise an m-bit digital word representing data (e.g., the timemeasurements) in m-dimensional space. In one embodiment, then-dimensional space associated with sub-string correlithm objects 1206aa-dd equals the m-dimensional space associated with sub-stringcorrelithm objects 1206 xx-zz. In other words, for example, thesub-string correlithm objects 1206 aa-dd and the sub-string correlithmobjects 1206 xx-zz may be all be represented by 64-bit digital words. Inanother embodiment, the n-dimensional space associated with sub-stringcorrelithm objects 1206 aa-dd may be different from the m-dimensionalspace associated with sub-string correlithm objects 1206 xx-zz. In otherwords, for example, the sub-string correlithm objects 1206 aa-dd may beall represented by 64-bit digital words whereas the sub-stringcorrelithm objects 1206 xx-zz may be all be represented by 128-bitdigital words.

FIG. 23 illustrates one embodiment of a correlithm object processingsystem 2300 that includes a sensor 302, a node 304, and an actor 306.System 2300 and its constituent components can be implemented byprocessor 502, one or more of the engines 510, 512, 514, and 522, andother elements of computer architecture 500, described above withrespect to FIG. 5. In general, node 304 receives an input correlithmobject 2302 that represents a task to be performed. This task maycomprise a series of sub-tasks that are represented by a stringcorrelithm object 2304 and sub-string correlithm objects 2306 a-d.Memory 504 of system 2300 stores data representing a “recording” ofprior experiences in correlithm objects 2310 a-f, string correlithmobject 2312, and string correlithm object 2316. Node 304 compares thecorrelithm objects 104 representing the tasks and sub-tasks with thecorrelithm objects 104 representing prior experiences in n-dimensionalspace. Actor 306 outputs real-world data 2320 to perform the tasks basedon the results of these comparisons. The use of correlithm objects 104to compare tasks and sub-tasks to be performed with prior experiencesenables system 2300 to perform analogous reasoning such as, for example,for artificial intelligence applications. In this way, system 2300 can“playback” prior experiences to perform future tasks and sub-tasks.Sensor 302 monitors real-world data 2322 as the tasks and sub-tasks arebeing performed, and works with node 304 to address gaps orinconsistencies in the analogous reasoning. Conventional artificialintelligence systems are unable to perform this sort of analogousreasoning that is powered by correlithm objects 104, as explained below.In a particular embodiment, the analogous reasoning enabled by system2300 may be used to control movements and operations of a machine 2330,such as a robot in one embodiment.

Input correlithm object 2302 represents a task to be performed. In oneembodiment, the task may be any suitable movement or operation to beperformed by a machine 2330, such as a robot. The task may involve aseries of individual sub-tasks that may be independent of each other orthat may be dependent upon each other to perform the task. Thesesub-tasks are collectively represented in FIG. 23 by string correlithmobject 2304 and individually represented by each of sub-stringcorrelithm objects 2306 a-d of string correlithm object 2304. Althoughstring correlithm object 2304 is illustrated with four sub-stringcorrelithm objects 2306 a-d, it may include any number and combinationof sub-string correlithm objects as appropriate for the particularapplication. For example, the task may be to control a robot to purchasea gallon of milk in a grocery store. In this example, the sub-tasks mayinvolve “buying milk in a particular grocery store,” “locating where inthe particular grocery store the milk can be found,” “picking up andcarrying the gallon of milk to the checkout counter,” and “paying forthe gallon of milk,” among others.

Correlithm objects 2310 a-f stored in memory 504 represent datadetailing a range of prior experiences which might be useful to performthe specific task associated with input correlithm object 2302. Forexample, correlithm object 2310 d may represent data detailing a priorexperience of “buying bread in a grocery store”; correlithm object 2310e may represent data detailing a prior experience of “buying milk in agas station convenience store”; and correlithm object 2310 f mayrepresent data detailing a prior experience of “buying milk in a grocerystore.” Correlithm objects 2310 a-c may represent data that is moregenerally associated with a prior experience of buying goods. Forexample, correlithm object 2310 a may represent data detailing a priorexperience of “looking for a product when it cannot be found”;correlithm object 2310 b may represent data detailing a prior experienceof “asking for help when a product cannot be found”; and correlithmobject 2310 c may represent data detailing a prior experience of “givingup and going home when a product cannot be found.”

Node 304 receives the input correlithm object 2302 as well as thesub-string correlithm objects 2306 a-d that represent the tasks andsub-tasks to be performed, and determines which prior experience is mostanalogous to the task to be performed. To do this, node 304 comparesinput correlithm object 2302 with the plurality of correlithm objects2310 a-f to identify the particular correlithm object 2310 that isclosest in n-dimensional space to input correlithm object 2302. Inparticular, node 304 determines the distances in n-dimensional spacebetween input correlithm object 2302 and each of the plurality ofcorrelithm objects 2310. In one embodiment, these distances may bedetermined by calculating Hamming distances between input correlithmobject 2302 and each of the plurality of correlithm objects 2310. Inanother embodiment, these distances may be determined by calculating theanti-Hamming distances between input correlithm object 2302 and each ofthe plurality of correlithm objects 2310.

As described above, the Hamming distance is determined based on thenumber of bits that differ between the binary string representing inputcorrelithm object 2302 and each of the binary strings representing eachof the correlithm objects 2310 a-f. The anti-Hamming distance may bedetermined based on the number of bits that are the same between thebinary string representing input correlithm object 2302 and each of thebinary strings representing each of the correlithm objects 2310 a-f. Instill other embodiments, the distances in n-dimensional space betweeninput correlithm object 2302 and each of the correlithm objects 2310 a-fmay be determined using a Minkowski distance or a Euclidean distance.

Upon calculating the distances between input correlithm object 2302 andeach of the plurality of correlithm objects 2310 a-f using one of thetechniques described above, node 304 determines which calculateddistance is the shortest distance. This is because the correlithm object2310 having the shortest distance between it and input correlithm object2302 received by node 304 can be thought of as being the most analogousprior experience to the task being performed. In the example describedherein, the correlithm object 2310 f representing data detailing theprior experience of “buying milk in a grocery store” is the mostanalogous to the task represented by input correlithm object 2302. Thus,system 2300 communicates correlithm object 2310 f to actor 306.

In the embodiment illustrated in FIG. 23, correlithm object 2310 f isitself associated with a string correlithm object 2312 that includes aplurality of sub-string correlithm objects 2314 a-d. Because the priorexperience represented by correlithm object 2310 f was the mostanalogous to the task represented by input correlithm object 2302, node304 may access the prior experiences associated with sub-stringcorrelithm objects 2314 a-d in conjunction with analyzing sub-tasks ofthe task, as described in greater detail below. In the example presentedherein, sub-string correlithm object 2314 a may represent “buying milkin conjunction with buying other groceries”; sub-string correlithmobject 2314 b may represent “buying only milk, but in a differentgrocery store” from the one represented by input correlithm object 2302;sub-string correlithm object 2314 c may represent “buying only milk, andin the same grocery store” as the one represented by input correlithmobject 2302; and sub-string correlithm object 2314 d may represent“buying only milk, but in a specialty grocery store.”

The task represented by input correlithm object 2302 is made up of aseries of sub-tasks represented by sub-string correlithm objects 2306a-d. In the example described herein, the sub-task of “buying milk in aparticular grocery store” may be represented by sub-string correlithmobject 2306 a; “locating where in the particular grocery store the milkcan be found” may be represented by sub-string correlithm object 2306 b;“picking up and carrying the gallon of milk to the checkout counter” maybe represented by sub-string correlithm object 2306 c; and “paying forthe gallon of milk” may be represented by sub-string correlithm object2306 d. Although only four sub-tasks are described in conjunction withthis example, additional or fewer sub-tasks may be provided, asappropriate, together with additional or fewer sub-string correlithmobjects 2306 to represent those sub-tasks.

Node 304 receives sub-string correlithm object 2306 a and determineswhich prior experience among those represented by sub-string correlithmobjects 2314 a-d is most analogous to the sub-task to be performed. Todo this, node 304 compares input sub-string correlithm object 2306 awith the plurality of sub-string correlithm objects 2314 a-d to identifythe particular sub-string correlithm object 2314 that is closest inn-dimensional space to input sub-string correlithm object 2306 a. Inparticular, node 304 determines the distances in n-dimensional spacebetween input sub-string correlithm object 2306 a and each of thesub-string correlithm objects 2314 a-d.

Upon calculating the distances between input sub-string correlithmobject 2306 a and each of the plurality of sub-string correlithm objects2314 a-d using one of the techniques described above, node 304determines which calculated distance is the shortest distance. This isbecause the sub-string correlithm object 2314 having the shortestdistance between it and input sub-string correlithm object 2306 areceived by node 304 can be thought of as being the most analogous priorexperience to the sub-task being performed. In the example describedherein, the sub-string correlithm object 2314 c representing datadetailing the prior experience of “buying milk in the same grocerystore” is the most analogous to the sub-task represented by inputsub-string correlithm object 2306 a. Thus, system 2300 communicatessub-string correlithm object 2314 c to actor 306 which outputsreal-world data 2320 a representing the prior experience of “buying milkin the same grocery” as indicated by the task. This real-world data 2320a may be used by a robot or other machine 2330 in conjunction with otherreal-world data to perform at least a portion of a task or sub-task.

Sub-string correlithm object 2314 c is associated with a stringcorrelithm object 2316 that includes a plurality of sub-stringcorrelithm objects 2318 a-d. Because the prior experience represented bycorrelithm object 2314 c was the most analogous to the sub-taskrepresented by input sub-string correlithm object 2306 a, node 304 mayaccess the prior experiences associated with sub-string correlithmobjects 2318 a-d in conjunction with analyzing additional sub-tasks ofthe task, as described in greater detail below. In the example presentedherein, sub-string correlithm object 2318 a may represent data detailingthe “general layout of the grocery store”; sub-string correlithm object2318 b may represent data for “locating the refrigerator unit storingmilk in the back of the grocery store”; sub-string correlithm object2318 c may represent data for “paying for the gallon of milk at aself-checkout counter”; and sub-string correlithm object 2318 d mayrepresent data for “paying for the gallon of milk at a full servicecheckout counter.”

Node 304 receives sub-string correlithm object 2306 b and determineswhich prior experience among those represented by sub-string correlithmobjects 2318 a-d is most analogous to the sub-task to be performed. Todo this, node 304 compares input sub-string correlithm object 2306 bwith the plurality of sub-string correlithm objects 2318 a-d to identifythe particular sub-string correlithm object 2318 that is closest inn-dimensional space to input sub-string correlithm object 2306 b. Inparticular, node 304 determines the distances in n-dimensional spacebetween input sub-string correlithm object 2306 b and each of thesub-string correlithm objects 2318 a-d.

Upon calculating the distances between input sub-string correlithmobject 2306 b and each of the plurality of sub-string correlithm objects2318 a-d using one of the techniques described above, node 304determines which calculated distance is the shortest distance. This isbecause the sub-string correlithm object 2318 having the shortestdistance between it and input sub-string correlithm object 2306 breceived by node 304 can be thought of as being the most analogous priorexperience to the sub-task being performed. In the example describedabove, the sub-string correlithm object 2318 b representing dataassociated with the prior experience of “locating the refrigerator unitstoring milk in the back of the grocery store” is the most analogous tothe sub-task represented by input sub-string correlithm object 2306 b.Thus, system 2300 communicates sub-string correlithm object 2318 b toactor 306 which outputs real-world data 2320 b representing the priorexperience of “locating the refrigerator unit storing milk in the backof the grocery store.” This real-world data 2320 b may be used by arobot or other machine 2330 in conjunction with other real-world data toperform at least a portion of a task or sub-task. For example, thisreal-world data 2320 b may be used to instruct the robot to walk to theback of the grocery store where a prior experience suggested the gallonof milk may be stored in a refrigerator unit. Note that the priorexperience for “locating the refrigerator unit storing milk in the backof the grocery store” need not be a hard-coded instruction for themachine 2330 to follow. It also need not be an exact match to the taskor specific sub-task to be performed. Instead, it may simply beanalogous to the task or sub-task to be performed. The use of correlithmobjects 104 to compare prior experiences and tasks/sub-tasks inn-dimensional space enables system 2300 to find analogous priorexperiences which may be used to control a machine 2330, rather thanrequiring an exact match between tasks/sub-tasks and a prior experience.This provides a technical advantage over conventional artificialintelligence systems, such as ones that are used to control machinery2330.

A sensor 302 may be used to address inconsistencies between real-worlddata and the prior experiences determined to be analogous totasks/sub-tasks. In the example described herein, a sensor 302positioned on the robot may be used to observe real-world data 2322 thatdetermines whether the milk is indeed located in the back of the grocerystore. For example, the real-world data 2322 may be visual data capturedby a camera on a robot that walked to the back of the grocery store.This real-world data 2322 may indicate that rather than storing milk inthe back of the grocery store, the refrigerator unit that the robotfinds itself in front of actually stores eggs. In other words, thereal-world data 2322 may indicate that the gallon of milk is notactually found where prior experiences suggested it should be found.Thus, the real-world data 2322 may be inconsistent with the real-worlddata 2320 b indicating that the refrigerator unit storing milk islocated in the back of the grocery store. At this point, sensor 302 mayconvert the real-world data 2322 indicating that the milk was not foundin the back of the grocery store into an intermediate input correlithmobject 2324.

Node 304 receives the intermediate input correlithm object 2324, anddetermines which prior experience is most analogous to it. For example,node 304 determines which prior experience is most analogous to notbeing able to find the milk in the grocery store. To do this, node 304compares intermediate input correlithm object 2324 with the plurality ofcorrelithm objects 2310 a-f to identify the particular correlithm object2310 that is closest in n-dimensional space to intermediate inputcorrelithm object 2324. In particular, node 304 determines the distancesin n-dimensional space between intermediate input correlithm object 2324and each of the plurality of correlithm objects 2310.

Upon calculating the distances between intermediate input correlithmobject 2324 and each of the plurality of correlithm objects 2310, node304 determines which calculated distance is the shortest distance. Inthe example described herein, the correlithm object 2310 b representingdata detailing the prior experience of “looking for a product when itcannot be found” is the most analogous to the condition represented byintermediate input correlithm object 2324. Thus, system 2300communicates correlithm object 2310 b to actor 306 which outputsreal-world data 2320 c representing the prior experience of “looking fora product when it cannot be found.” This real-world data 2320 c may beused by a robot or other machine 2330 in conjunction with otherreal-world data to perform at least a portion of a task or sub-task.

Upon looking for the milk and, perhaps, finding the milk in a differentrefrigerator unit in the back of the grocery store, the operation canreturn to performing the remaining sub-tasks, such as “picking up andcarrying the gallon of milk to the checkout counter” as represented bysub-string correlithm object 2306 c, and “paying for the gallon of milk”as represented by sub-string correlithm object 2306 d. The sensor 302,node 304, and actor 306 work harmoniously to compare tasks/sub-tasks tobe performed with prior experiences, to produce real-world data 2320used to control the operation of machines 2330, and to monitorreal-world data 2322 in order to fill in any gaps or correct for anyinconsistencies with real-world data 2320.

The operation of system 2300 was described above with respect to aparticular example to illustrate how particular tasks/sub-tasks can becompared to prior experiences in n-dimensional space using correlithmobjects 104 to emulate analogous reasoning in an artificial intelligenceapplication. The operation of system 2300 is not limited to the specificexample described herein.

FIG. 24 is a flowchart of an embodiment of a process 2400 for comparingtasks/sub-tasks with prior experiences using correlithm objects 104 toperform analogous reasoning such as, for example, in an artificialintelligence application. At step 2410, correlithm objects 104representing prior experiences are stored such as, for example, in amemory 504. Referring back to FIG. 23, these correlithm objects 104 maybe logically organized in a hierarchy that includes any number andcombination of multiple levels of correlithm objects 104. For example,correlithm objects 104 may include a plurality of correlithm objects2310. One or more of these correlithm objects 2310 may be associatedwith multiple levels of string correlithm objects. For example, asillustrated in FIG. 23, correlithm object 2310 f includes a first levelstring correlithm object 2312 that itself comprises a plurality of firstlevel sub-string correlithm objects 2314 a-d. Similarly, one or more ofthese first level sub-string correlithm objects 2314 a-d also includes asecond level string correlithm object 2316 that itself comprises aplurality of second level sub-string correlithm objects 2318 a-d.Although the correlithm objects 104 stored at step 2410 are illustratedas being organized in a hierarchical relationship, they may be stored inany suitable logical organization that facilitates interrelationshipsand access by the other elements of system 2300, such as node 304.

Execution proceeds to step 2412 where the process 2400 receives an inputcorrelithm object 104 representing a task/sub-task. Referring back toFIG. 23, for example, node 304 may receive input correlithm object 2302representing a task to be performed. Node 304 may also receive one ormore input sub-string correlithm objects 2306 a-d representing sub-tasksto be performed.

At step 2414, the process 2400 determines distances in n-dimensionalspace between the received input correlithm object 104 representing thetask/sub-task and the stored correlithm objects 104 representing priorexperiences. Referring back to FIG. 23, for example, node 304 maydetermine the n-dimensional distance between input correlithm objects2302 and each of the plurality of correlithm objects 2310 a-f to findthe prior experience that is the closest match to the task to beperformed. In another example, node 304 may determine the n-dimensionaldistance between input sub-string correlithm objects 2306 a-d andsub-string correlithm objects 2314 a-d and/or 2318 a-d. As explainedabove, these n-dimensional distances may be determined using Hammingdistances, anti-Hamming distances, Minkowski distances, and/or Euclideandistances. By comparing correlithm objects 104 in n-dimensional space,process 2400 can find prior experiences that are most analogous to thetask/sub-task to be performed instead of having to find an exact match.

At step 2416, process 2400 identifies the correlithm object 104 with theshortest distance in n-dimensional space, which indicates the priorexperience that is most analogous to the task/sub-task to be performed.Referring back to FIG. 23, for example, node 304 determines which ofcorrelithm objects 2310 a-f has the shortest distance in n-dimensionalspace to the input correlithm object 2302 representing a task. Inanother example, node 304 determines which of sub-string correlithmobjects 2314 a-d and/or 2318 a-d has the shortest distance inn-dimensional space to a particular input sub-string correlithm object2306 a-d representing a sub-task.

At step 2418, process 2400 outputs real-world data associated with thecorrelithm object 104 identified at step 2416. Referring back to FIG.23, for example, actor 306 may receive one or more of correlithm objects2310 b, 2314 c and 2318 b and output real-world data 2320 a-c. In aparticular embodiment, execution proceeds to step 2420 where process2400 controls machine 2330 using real-world data 2320. For example,machine 2330 may be a robot that is controlled, at least in part, basedupon the real-world data 2320 output by actor 306 in combination withany other instructions or programming, as appropriate. At step 2422,process 2400 monitors real-world data. Referring back to FIG. 23, forexample, sensor 302 may receive real-world data 2322 as the tasks andsub-tasks are being performed, and works with node 304 to address gapsor inconsistencies in the analogous reasoning. Execution proceeds tostep 2424 where process 2400 determines whether the real-world datamonitored at step 2422 is inconsistent with any real-world data outputat step 2418. If yes, then execution proceeds to step 2426 where process2400 receives an intermediate input correlithm object 2426 associatedwith the real-world data 2322 monitored at step 2422, and executionreturns to step 2414. If no, execution proceeds to step 2428 whereprocess 2400 determines whether any sub-tasks represented by sub-stringcorrelithm objects 2306 remain to be processed. If yes, executionreturns to step 2412. If no, execution concludes at step 2430.

The relationships between and among data elements can span multipledimensions of n-dimensional space 102. Moreover, multiple data elements(e.g., real-world data or data already represented by correlithm objects104) often have interrelationships between them that cannot always becaptured in a linear geometric relationship. For example, data elementsA, B, and C may have interrelationships such that data element A iscorrelated with data element B by a certain degree and data element B iscorrelated with data element C by a certain degree, but data element Ais correlated with data element C by an amount that is not necessarilythe sum of the amount of correlation of data elements A with B and dataelements B with C. Accordingly, a technique to map the correlationsbetween multiple data elements that have other geometric relationshipsis desirable. A lattice correlithm object and/or a bidirectional stringcorrelithm object, as described below, may be used to capture these morecomplex interrelationships between and among data elements inn-dimensional space 102.

FIG. 25 illustrates one embodiment of a triangle lattice correlithmobject generator 2500 configured to generate a triangle latticecorrelithm object 2510 as output. Triangle lattice correlithm objectgenerator 2500 and its constituent components is implemented by latticecorrelithm object engine 524, and any other suitable elements ofcomputer architecture 500, described above with respect to FIG. 5.Triangle lattice correlithm object generator 2500 comprises a firstprocessing stage 2502 a, a second processing stage 2502 b, and a thirdprocessing stage 2502 c communicatively coupled to each other. Firstprocessing stage 2502 a receives an input 2504 and outputs a firstsub-lattice correlithm object 2506 a that comprises an n-bit digitalword wherein each bit has either a value of zero or one. In oneembodiment, first processing stage 2502 a generates the values of eachbit randomly. Input 2504 comprises one or more parameters used todetermine the characteristics of the lattice correlithm object 2510. Forexample, input 2504 may include a parameter for the number ofdimensions, n, in the n-dimensional space 102 (e.g., 64, 128, 256, etc.)in which to generate the lattice correlithm object 2510. Input 2504 mayalso include a distance parameter, δ, that indicates a particular numberof bits of the n-bit digital word (e.g., 4, 8, 16, etc.) that will bechanged from one sub-lattice correlithm object 2506 to the next in thelattice correlithm object 2510. Although FIG. 25 illustrates only firstprocessing stage 2502 a receiving input 2504, it should be understoodthat both second processing stage 2502 b and third processing stage 2502c may also receive input 2504 (or at least the information containedwithin input 2504).

In the examples described below, assume that n=64 such that eachsub-lattice correlithm object 2506 of the lattice correlithm object 2510is a 64-bit digital word. As discussed previously with regard to FIG. 9,the standard deviation is equal to

$\sqrt{\frac{n}{4}},$

or four bits, for a 64-dimensional space 102. In one embodiment, thedistance parameter, δ, is selected to equal the standard deviation. Inthis embodiment, the distance parameter is also four bits (e.g., onestandard deviation). Although in the examples described below theselected distance parameter is four bits, other distance parameters maybe used in input 2504 depending on the particular structure andarrangement desired for triangle lattice correlithm object 2510. Ingeneral, the fewer the number of bits that are changed betweensub-lattice correlithm objects 2506, the tighter the correlation betweenthose sub-lattice correlithm objects 2506; and the greater the number ofbits that are changed between sub-lattice correlithm objects 2506, thelooser the correlation between those sub-lattice correlithm objects2506. Moreover, it is not necessary that each processing stage 2502 oftriangle correlithm object generator 2500 strictly change the exactnumber of bits identified by the distance parameter from one sub-latticecorrelithm object 2506 to the next. Instead, the present disclosurecontemplates some variance from the number of bits indicated by thedistance parameter (e.g., a fewer or a greater number of bits beingchanged).

Second processing stage 2502 b receives the first sub-lattice correlithmobject 2506 a and, for a plurality of bits of the first sub-latticecorrelithm object 2506 a up to the particular number of bits identifiedin the distance parameter, δ, changes the value from a zero to a one orfrom a one to a zero to generate a second sub-lattice correlithm object2506 b. In this example, four bit values are changed from the firstsub-lattice correlithm object 2506 a to generate the second sub-latticecorrelithm object 2506 b. The bits of the first sub-lattice correlithmobject 2506 a that are changed in value to generate the secondsub-lattice correlithm object 2506 b may be selected randomly from then-bit digital word. The other bits of the n-bit digital word in secondsub-lattice correlithm object 2506 b remain the same values as thecorresponding bits of the first sub-lattice correlithm object 2506 a. Bychanging four bit values, the core of the first sub-lattice correlithmobject 2506 a overlaps in 64-dimensional space with the core of thesecond sub-lattice correlithm object 2506 b. Third processing stage 2502c receives the second sub-lattice correlithm object 2506 b and modifiescertain bits of the second sub-lattice correlithm object 2506 b togenerate a third sub-lattice correlithm object 2506 c, as describedbelow with respect to different embodiments.

General Triangle Lattice Correlithm Object

In one embodiment, the third processing stage 2502 c may receive thesecond sub-lattice correlithm object 2506 b and, for a plurality of bitsof the second sub-lattice correlithm object 2506 b up to the particularnumber of bits identified in the distance parameter, δ, changes thevalue from a zero to a one or from a one to a zero to generate a thirdsub-lattice correlithm object 2506 c. In this example, four bit valuesare changed from the second sub-lattice correlithm object 2506 b togenerate the third sub-lattice correlithm object 2506 c. The bits of thesecond sub-lattice correlithm object 2506 b that are changed in value togenerate the third sub-lattice correlithm object 2506 c may be selectedrandomly from the n-bit digital word. The other bits of the n-bitdigital word in third sub-lattice correlithm object 2506 c remain thesame values as the corresponding bits of the second sub-latticecorrelithm object 2506 b.

FIG. 26A illustrates a table 2520 a that demonstrates the changes in bitvalues from a first sub-lattice correlithm object 2506 a to a secondsub-lattice correlithm object 2506 b, and from a second sub-latticecorrelithm object 2506 b to a third sub-lattice correlithm object 2506c, according to this embodiment. For example, the first column of table2520 a illustrates at least a portion of the bit values of then-dimensional first sub-lattice correlithm object 2506 a generated byfirst processing stage 2502 a. The second column of table 2520 aillustrates at least a portion of the bit values of the n-dimensionalsecond sub-lattice correlithm object 2506 b generated by secondprocessing stage 2502 b, with four randomly selected bits with valueschanged from a zero to a one, or from a one to a zero. The third columnof table 2520 a illustrates at least a portion of the bit values of then-dimensional third sub-lattice correlithm object 2506 c generated bythird processing stage 2502 c, with another four randomly selected bitswith values changed from a zero to a one, or from a one to a zero.

FIG. 27A illustrates one embodiment of a lattice correlithm object 2510with a generally triangular shape and formed by first sub-latticecorrelithm object 2506 a, second sub-lattice correlithm object 2506 bthat is four bits away from first sub-lattice correlithm object 2506 ain n-dimensional space 102, and third sub-lattice correlithm object 2506c that is four bits away from second sub-lattice correlithm object 2506b in n-dimensional space 102. Third sub-lattice correlithm object 2506 cmay be an undefined number of bits away from first sub-latticecorrelithm object 2506 a in n-dimensional space 102. This is because theparticular bits that are changed by second processing stage 2506 b whencreating second sub-lattice correlithm object 2506 bfrom firstsub-lattice 2506 a are randomly selected, and because the particularbits that are changed by third processing stage 2506 c when creatingthird sub-lattice correlithm object 2506 c from second sub-lattice 2506b are randomly selected. Therefore, third sub-lattice correlithm object2506 c may be anywhere between zero and eight bits away from firstsub-lattice correlithm object 2506 a in n-dimensional space 102.

Referring back to FIG. 25, the third processing stage 2502 c may receivefrom itself the third sub-lattice correlithm object 2506 c and modifycertain bits to generate a fourth sub-lattice correlithm object 2506 d.For example, for a plurality of bits of the third sub-lattice correlithmobject 2506 c up to the particular number of bits identified by thedistance parameter, δ, the third processing stage 2502 c changes thevalue from a zero to a one or from a one to a zero. In this example,four bit values are changed from the third sub-lattice correlithm object2506 c to generate the fourth sub-lattice correlithm object 2506 d. Thebits of the third sub-lattice correlithm object 2506 c that are changedin value to generate the fourth sub-lattice correlithm object 2506 d maybe selected randomly from the n-bit digital word. The other bits of then-bit digital word in fourth sub-lattice correlithm object 2506 d remainthe same values as the corresponding bits of the third sub-latticecorrelithm object 2506 c. The third processing stage 2502 c maysuccessively output a subsequent sub-lattice correlithm object 2506based on changing bit values of the immediately prior sub-latticecorrelithm object 2506 received as feedback, as described herein.

Equilateral Triangle Lattice Correlithm Object

In another embodiment, the third processing stage 2502 c may receive thesecond sub-lattice correlithm object 2506 b and, for each bit of thesecond sub-lattice correlithm object 2506 b that was changed from thefirst sub-lattice correlithm object 2506 a up to half of the number ofbits identified by the distance parameter, δ, changes the value from azero to a one or from a one to a zero. Thus, in this example, two of thebits that were changed from first sub-lattice correlithm object 2506 ato second sub-lattice correlithm object 2506 b are changed to generatethird sub-lattice correlithm object 2506 c. Moreover, for each bit ofthe second sub-lattice correlithm object 2506 b that remained the samefrom the first sub-lattice correlithm object 2506 a up to half of thenumber of bits identified by the distance parameter, δ, the thirdprocessing stage 2502 c changes the value from a zero to a one or from aone to a zero. Thus, in this example, two of the bits that remained thesame from first sub-lattice correlithm object 2506 a to secondsub-lattice correlithm object 2506 b are changed to generate thirdsub-lattice correlithm object 2506 c. The other bits of the n-bitdigital word in third sub-lattice correlithm object 2506 c remain thesame values as the corresponding bits of the second sub-latticecorrelithm object 2506 b.

FIG. 26B illustrates a table 2520 b that demonstrates the changes in bitvalues from a first sub-lattice correlithm object 2506 a to a secondsub-lattice correlithm object 2506 b, and from a second sub-latticecorrelithm object 2506 b to a third sub-lattice correlithm object 2506c, according to this embodiment. The first column of table 2520 billustrates at least a portion of the bit values of the n-dimensionalfirst sub-lattice correlithm object 2506 a generated by first processingstage 2502 a. The second column of table 2520 b illustrates at least aportion of the bit values of the n-dimensional second sub-latticecorrelithm object 2506 b generated by second processing stage 2502 b,with four randomly selected bits with values changed from a zero to aone, or from a one to a zero. The third column of table 2520 billustrates at least a portion of the bit values of the n-dimensionalthird sub-lattice correlithm object 2506 c generated by third processingstage 2502 c, whereby for each bit of the second sub-lattice correlithmobject 2506 b that was changed from the first sub-lattice correlithmobject 2506 a, up to half of the number of bits identified by thedistance parameter, δ, are changed from a zero to a one or from a one toa zero. Thus, as illustrated in the second column of table 2520 b, fourbit values were changed from first sub-lattice correlithm object 2506 ato create second sub-lattice correlithm object 2506 b, and two of thosebit values are further changed to create third sub-lattice correlithmobject 2506 c. The third column of table 2520 b also illustrates thatfor each bit of the second sub-lattice correlithm object 2506 b thatremained the same from the first sub-lattice correlithm object 2506 a,up to half of the number of bits identified by the distance parameter,δ, are changed from a zero to a one or from a one to a zero. Thus, asillustrated in the second column of table 2520 b, sixty bit valuesremained the same from first sub-lattice correlithm object 2506 a tocreate second sub-lattice correlithm object 2506 b and two of those bitvalues are changed to create third sub-lattice correlithm object 2506 c.The other bits of the n-bit digital word in third sub-lattice correlithmobject 2506 c remain the same values as the corresponding bits of thesecond sub-lattice correlithm object 2506 b.

FIG. 27B illustrates one embodiment of a lattice correlithm object 2510with an equilateral triangle shape and formed by first sub-latticecorrelithm object 2506 a, second sub-lattice correlithm object 2506 bthat is four bits away from first sub-lattice correlithm object 2506 ain n-dimensional space 102, and third sub-lattice correlithm object 2506c that is four bits away from second sub-lattice correlithm object 2506b in n-dimensional space 102. Because of the particular techniqueemployed by the third processing stage 2502 c described above, thirdsub-lattice correlithm object 2506 c is also four bits away from firstsub-lattice correlithm object 2506 a in n-dimensional space 102, whichleads to the equilateral triangle shape. This is because the particularbits that are changed by third processing stage 2502 c when creatingthird sub-lattice correlithm object 2506 c from second sub-lattice 2506b are specifically determined as described above.

Referring back to FIG. 25, the third processing stage 2502 c may receivefrom itself the third sub-lattice correlithm object 2506 c and modifycertain bits to generate a fourth sub-lattice correlithm object 2506 d.For example, for each bit of the third sub-lattice correlithm object2506 c that is different from the first sub-lattice correlithm object2506 a up to half of the number of bits identified by the distanceparameter, δ, the third processing stage 2502 c changes the value from azero to a one or from a one to a zero. In this example, two of the fourbits that are different between the third sub-lattice correlithm object2506 c and the first sub-lattice correlithm object 2506 a are changed togenerate fourth sub-lattice correlithm object 2506 d. Additionally, foreach bit of the third sub-lattice correlithm object 2506 c that is thesame from the first sub-lattice correlithm object 2506 a up to half ofthe number of bits identified by the distance parameter, δ, the thirdprocessing stage 2502 c changes the value from a zero to a one or from aone to a zero. In this example, two of the sixty bits that remained thesame between the third sub-lattice correlithm object 2506 c and thefirst sub-lattice correlithm object 2506 a are changed to generate thefourth sub-lattice correlithm object 2506 d. The other bits of the n-bitdigital word in fourth sub-lattice correlithm object 2506 d remain thesame values as the corresponding bits of the third sub-latticecorrelithm object 2506 c. The third processing stage 2502 c maysuccessively output a subsequent sub-lattice correlithm object 2506,such as sub-lattice correlithm objects 2506 e, 2506 f, and 2506 g asillustrated in FIG. 27D, based on changing bit values of the immediatelyprior sub-lattice correlithm object 2506 received as feedback, asdescribed herein. FIG. 27D illustrates one embodiment of a hexagonallattice correlithm object 2512 formed by generating and arranging sixlattice correlithm objects 2510 with an equilateral triangle shape,using the techniques described herein.

Isosceles Triangle Lattice Correlithm Object

In still another embodiment, the third processing stage 2502 c mayreceive the second sub-lattice correlithm object 2506 b and, for eachbit of the second sub-lattice correlithm object 2506 b that was changedfrom the first sub-lattice correlithm object 2506 a, changes the valuefrom a zero to a one or from a one to a zero. Thus, in this example, allfour of the bits that were changed from first sub-lattice correlithmobject 2506 a to second sub-lattice correlithm object 2506 b are changedto generate third sub-lattice correlithm object 2506 c. Moreover, foreach bit of the second sub-lattice correlithm object 2506 b thatremained the same from the first sub-lattice correlithm object 2506 a upto the number of bits identified by the distance parameter, δ, the thirdprocessing stage 2502 c changes the value from a zero to a one or from aone to a zero. Thus, in this example, four of the bits that remained thesame from the first sub-lattice correlithm object 2506 a to generatesecond sub-lattice correlithm object 2506 b are changed to generatethird sub-lattice correlithm object 2506 c. The other bits of the n-bitdigital word in third sub-lattice correlithm object 2506 c remain thesame values as the corresponding bits of the second sub-latticecorrelithm object 2506 b.

FIG. 26C illustrates a table 2520 c that demonstrates the changes in bitvalues from a first sub-lattice correlithm object 2506 a to a secondsub-lattice correlithm object 2506 b, and from a second sub-latticecorrelithm object 2506 b to a third sub-lattice correlithm object 2506c, according to this embodiment. The first column of table 2520 cillustrates at least a portion of the bit values of the n-dimensionalfirst sub-lattice correlithm object 2506 a generated by first processingstage 2502 a. The second column of table 2520 c illustrates at least aportion of the bit values of the n-dimensional second sub-latticecorrelithm object 2506 b generated by second processing stage 2502 b,with four randomly selected bits with values changed from a zero to aone, or from a one to a zero. The third column of table 2520 cillustrates at least a portion of the bit values of the n-dimensionalthird sub-lattice correlithm object 2506 c generated by third processingstage 2502 c, whereby each of the four bits of the second sub-latticecorrelithm object 2506 b that was changed from the first sub-latticecorrelithm object 2506 a, are changed from a zero to a one or from a oneto a zero. Thus, as illustrated in the second column of table 2520 c,four bit values were changed from first sub-lattice correlithm object2506 a to create second sub-lattice correlithm object 2506 b and each ofthose four bit values are further changed to create third sub-latticecorrelithm object 2506 c. The third column of table 2520 c alsoillustrates that for each bit of the second sub-lattice correlithmobject 2506 b that remained the same from the first sub-latticecorrelithm object 2506 a, the number of bits identified by the distanceparameter, δ, are changed from a zero to a one or from a one to a zero.Thus, as illustrated in the second column of table 2520 c, sixty bitvalues remained the same from first sub-lattice correlithm object 2506 ato create second sub-lattice correlithm object 2506 b and four of thosebit values are changed to create third sub-lattice correlithm object2506 c. The other bits of the n-bit digital word in third sub-latticecorrelithm object 2506 c remain the same values as the correspondingbits of the second sub-lattice correlithm object 2506 b.

FIG. 27C illustrates one embodiment of a lattice correlithm object 2510with a triangle shape and formed by first sub-lattice correlithm object2506 a, second sub-lattice correlithm object 2506 b that is four bitsaway from first sub-lattice correlithm object 2506 a in n-dimensionalspace 102, and third sub-lattice correlithm object 2506 c that is eightbits away from second sub-lattice correlithm object 2506 b inn-dimensional space 102 but only four bits away from first sub-latticecorrelithm object 2506 a in n-dimensional space 102. This is because theparticular bits that are changed by third processing stage 2502 c whencreating third sub-lattice correlithm object 2506 c from secondsub-lattice 2506 b are specifically determined as described above.

Referring back to FIG. 25, the third processing stage 2502 c may receivefrom itself the third sub-lattice correlithm object 2506 c and modifycertain bits to generate a fourth sub-lattice correlithm object 2506 d.For example, for each bit of the third sub-lattice correlithm object2506 c that is different from the first sub-lattice correlithm object2506 a, the third processing stage 2502 c changes the value from a zeroto a one or from a one to a zero. In this example, the four bits thatare different between the third sub-lattice correlithm object 2506 c andthe first sub-lattice correlithm object 2506 a are changed to generatefourth sub-lattice correlithm object 2506 d. Additionally, for each bitof the third sub-lattice correlithm object 2506 c that is the same fromthe first sub-lattice correlithm object 2506 a up to the number of bitsidentified by the distance parameter, δ, the third processing stage 2502c changes the value from a zero to a one or from a one to a zero. Inthis example, four of the sixty bits that remained the same between thethird sub-lattice correlithm object 2506 c and the first sub-latticecorrelithm object 2506 a are changed to generate the fourth sub-latticecorrelithm object 2506 d. The other bits of the n-bit digital word infourth sub-lattice correlithm object 2506 d remain the same values asthe corresponding bits of the third sub-lattice correlithm object 2506c. The third processing stage 2502 c may successively output asubsequent sub-lattice correlithm object 2506 based on changing bitvalues of the immediately prior sub-lattice correlithm object 2506received as feedback, as described herein.

FIG. 25 further illustrates a node 304 and node table 2513 stored inmemory 504. Node 304 receives the lattice correlithm object 2510,including each of the sub-lattice correlithm objects 2506, and furtherreceives data elements 2514 in the form of real-world data elements ordata represented by correlithm objects 104. Node table 2513 associatesthe data elements 2514 to the sub-lattice correlithm objects 2506 oflattice correlithm object 2510.

In a particular embodiment, lattice correlithm object 2510 may be usedto represent data elements A, B, and C in a non-linear and/ormulti-directional relationship. For example, the degree of relationshipsamong three people may be captured using a lattice correlithm object2510. In particular, a Person A may be represented by sub-latticecorrelithm object 2506 a; Person B may be represented by sub-latticecorrelithm object 2506 b; and Person C may be represented by sub-latticecorrelithm object 2506 c. The degree to which Person A knows Person Bmay be represented by the distance in n-dimensional space 102 betweensub-lattice correlithm objects 2506 a and 2506 b. The degree to whichPerson A knows Person C may be represented by the distance inn-dimensional space 102 between sub-lattice correlithm objects 2506 aand 2506 c. Finally, the degree to which Person B knows Person C may berepresented by the distance in n-dimensional space 102 betweensub-lattice correlithm objects 2506 b and 2506 c.

If Persons A, B, and C all know each other equally well, thensub-lattice correlithm objects 2506 a, 2506 b, and 2506 c will beequidistant from each other in n-dimensional space 102, as illustratedin FIG. 27B. If Person A knows Persons B and C equally well but Person Bknows Person C less well, then sub-lattice correlithm objects 2506 awill be equidistant to sub-lattice correlithm objects 2506 b and 2506 cin n-dimensional space 102, but sub-lattice correlithm object 2506 bwill be further away from sub-lattice correlithm object 2506 c, asillustrated in FIGS. 27B or 27C. The relationship between a Person A andsix other people that Person A knows equally well may be represented bythe hexagonal lattice correlithm object 2512 illustrated in FIG. 27D. Iftwo people, such as Persons B and C, don't know each other, thensub-lattice correlithm objects 2506 b and 2506 c may be a standarddistance apart from each other in n-dimensional space 102.

Moreover, multiple data models may be used to represent asymmetricdegrees of relationships among people from the perspective of specificpeople within that group. For example, two people can know each other todifferent degrees such that a Person A may feel as though he knowsPerson B better than Person B feels they know Person A. The same may betrue for the relationships between Persons A and C and/or Persons B andC. The asymmetric relationships between the same Persons A, B, and C canbe modeled well using correlithm objects by creating one latticecorrelithm object 2510 that models relationships with Persons B and Cfrom Person A's perspective, another lattice correlithm object 2510 thatmodels relationships with Persons A and C from Person B's perspective,and another lattice correlithm object 2510 that models relationshipswith Persons A and B from Person C's perspective.

Although these embodiments were described with respect to therelationships between people, it should be understood that a trianglelattice correlithm object 2510 and/or a hexagonal lattice correlithmobject 2512 may be used to represent many different types ofrelationships between many different types of data elements.

FIG. 28 illustrates one embodiment of a quadrilateral lattice correlithmobject generator 2800 configured to generate a quadrilateral latticecorrelithm object 2810 as output. Quadrilateral lattice correlithmobject generator 2800 and its constituent components is implemented bylattice correlithm object engine 524, and any other suitable elements ofcomputer architecture 500, described above with respect to FIG. 5.Quadrilateral lattice correlithm object generator 2800 comprises a firstprocessing stage 2802 a, a second processing stage 2802 b, a thirdprocessing stage 2802 c, and a fourth processing stage 2802 dcommunicatively coupled to each other. First processing stage 2802 areceives an input 2804 and outputs a first sub-lattice correlithm object2806 a that comprises an n-bit digital word wherein each bit has eithera value of zero or one. In one embodiment, first processing stage 2802 agenerates the values of each bit randomly. Input 2804 comprises one ormore parameters used to determine the characteristics of the latticecorrelithm object 2810. For example, input 2804 may include a parameterfor the number of dimensions, n, in the n-dimensional space 102 (e.g.,64, 128, 256, etc.) in which to generate the lattice correlithm object2810. Input 2804 may also include a distance parameter, δ, thatindicates a particular number of bits of the n-bit digital word (e.g.,4, 8, 16, etc.) that will be changed from one sub-lattice correlithmobject 2806 to the next in the lattice correlithm object 2810. AlthoughFIG. 28 illustrates only first processing stage 2802 a receiving input2804, it should be understood that second processing stage 2802 b, thirdprocessing stage 2802 c, and fourth processing stage 2802 d may alsoreceive input 2804 (or at least the information contained within input2804).

In the examples described below, assume that n=64 such that eachsub-lattice correlithm object 2806 of the lattice correlithm object 2810is a 64-bit digital word. As discussed previously with regard to FIG. 9,the standard deviation is equal to

$\sqrt{\frac{n}{4}},$

or four bits, for a 64-dimensional space 102. In one embodiment, thedistance parameter, δ, is selected to equal the standard deviation. Inthis embodiment, the distance parameter is also four bits (e.g., onestandard deviation). Although in the examples described below, theselected distance parameter is four bits, other distance parameters maybe used in input 2804 depending on the particular structure andarrangement desired for quadrilateral lattice correlithm object 2810. Ingeneral, the fewer the number of bits that are changed betweensub-lattice correlithm objects 2806, the tighter the correlation betweenthose sub-lattice correlithm objects 2806; and the greater the number ofbits that are changed between sub-lattice correlithm objects 2806, thelooser the correlation between those sub-lattice correlithm objects2806. Moreover, it is not necessary that each processing stage 2802 ofquadrilateral correlithm object generator 2800 strictly change the exactnumber of bits identified by the distance parameter from one sub-latticecorrelithm object 2806 to the next. Instead, the present disclosurecontemplates some variance from the number of bits indicated by thedistance parameter (e.g., a fewer or a greater number of bits beingchanged).

Second processing stage 2802 b receives the first sub-lattice correlithmobject 2806 a and, for a plurality of bits of the first sub-latticecorrelithm object 2806 a up to the particular number of bits identifiedin the distance parameter, δ, changes the value from a zero to a one orfrom a one to a zero to generate a second sub-lattice correlithm object2806 b. In this example, four bit values are changed from the firstsub-lattice correlithm object 2806 a to generate the second sub-latticecorrelithm object 2806 b. The bits of the first sub-lattice correlithmobject 2806 a that are changed in value to generate the secondsub-lattice correlithm object 2806 b may be selected randomly from then-bit digital word. The other bits of the n-bit digital word in secondsub-lattice correlithm object 2806 b remain the same values as thecorresponding bits of the first sub-lattice correlithm object 2806 a. Bychanging four bit values, the core of the first sub-lattice correlithmobject 2806 a overlaps in n-dimensional space 102 with the core of thesecond sub-lattice correlithm object 2806 b.

Third processing stage 2802 c receives the first sub-lattice correlithmobject 2806 a and, for a plurality of bits of the first sub-latticecorrelithm object 2806 a up to the particular number of bits identifiedin the distance parameter, δ, changes the value from a zero to a one orfrom a one to a zero to generate a third sub-lattice correlithm object2806 c. In this example, four bit values are changed from the firstsub-lattice correlithm object 2806 a to generate the third sub-latticecorrelithm object 2806 c. The bits of the first sub-lattice correlithmobject 2806 a that are changed in value to generate the thirdsub-lattice correlithm object 2806 c may be selected randomly from then-bit digital word. However, the bits of the first sub-latticecorrelithm object 2806 a that are changed in value by the thirdprocessing stage 2802 c are different from the bits of the firstsub-lattice correlithm object 2806 a that are changed in value by thesecond processing stage 2802 b. The other bits of the n-bit digital wordin third sub-lattice correlithm object 2806 c remain the same values asthe corresponding bits of the first sub-lattice correlithm object 2806a. By changing four bit values, the core of the first sub-latticecorrelithm object 2806 a overlaps in n-dimensional space 102 with thecore of the third sub-lattice correlithm object 2806 c.

Fourth processing stage 2502 d receives the second sub-latticecorrelithm object 2506 b and the third sub-lattice correlithm object2506 c, and modifies certain bits of the second sub-lattice correlithmobject 2506 b and the third sub-lattice correlithm object 2506 c togenerate a fourth sub-lattice correlithm object 2506 d, as describedbelow with respect to different embodiments.

General Quadrilateral Lattice Correlithm Object

In one embodiment, the fourth processing stage 2802 d may receive thefirst sub-lattice correlithm object 2806 a, the second sub-latticecorrelithm object 2806 b, and the third sub-lattice correlithm object2806 c. The fourth processing stage 2802 d outputs a fourth correlithmobject 2806 d where each bit of the fourth sub-lattice correlithm object2806 d has a value that is based on the value of a corresponding bit ofthe first sub-lattice correlithm object 2806 a that was not changed inthe second sub-lattice correlithm object 2806 b by the second processingstage 2802 b and that was not changed in the third sub-latticecorrelithm object 2806 c by the third processing stage 2802 c. Thevalues of the bits of the first sub-lattice correlithm object 2806 athat were changed in the second sub-lattice correlithm object 2806 b andin the third sub-lattice correlithm object 2806 c are further modifiedas follows. For each bit of the second sub-lattice correlithm object2806 b that was changed from the first sub-lattice correlithm object2806 a, the fourth processing stage 2802 d changes from a zero to a oneor from a one to a zero a portion of those bits and keeps the value thesame as in the second sub-lattice correlithm object 2806 b for aremaining portion of those bits. Thus, for example, if four bits werechanged from the first sub-lattice correlithm object 2806 a to thesecond sub-lattice correlithm object 2806 b, then some or all of thosefour bits may be further changed when creating the fourth sub-latticecorrelithm object 2806 d. For each bit of the third sub-latticecorrelithm object 2806 c that was changed from the first sub-latticecorrelithm object 2806 a, the fourth processing stage 2802 d changes thevalue from a zero to a one or from a one to a zero for a portion ofthose bits and keeps the value the same as in the third sub-latticecorrelithm object 2806 c for a remaining portion of those bits. Thus,for example, if four bits were changed from the first sub-latticecorrelithm object 2806 a to the third sub-lattice correlithm object 2806b, then some or all of those four bits may be further changed whencreating the fourth sub-lattice correlithm object 2806 d.

FIG. 29A illustrates a table 2820 a that demonstrates the changes in bitvalues from one sub-lattice correlithm object 2806 to anothersub-lattice correlithm object 2806. For example, the first column oftable 2820 a illustrates at least a portion of the bit values of then-dimensional first sub-lattice correlithm object 2806 a generated byfirst processing stage 2802 a. The second column of table 2820 aillustrates at least a portion of the bit values of the n-dimensionalsecond sub-lattice correlithm object 2806 b generated by secondprocessing stage 2802 b, with four randomly selected bits with valueschanged from a zero to a one, or from a one to a zero. The third columnof table 2820 a illustrates at least a portion of the bit values of then-dimensional third sub-lattice correlithm object 2806 c generated bythird processing stage 2802 c, with another four randomly selected bitswith values changed from a zero to a one, or from a one to a zero. Thebits of the first sub-lattice correlithm object 2806 a that are changedin value to generate the third sub-lattice correlithm object 2806 c maybe selected randomly from the n-bit digital word. However, the bits ofthe first sub-lattice correlithm object 2806 a that are changed in valueto generate the third sub-lattice correlithm object 2802 c are differentfrom the bits of the first sub-lattice correlithm object 2806 a that arechanged in value to generate the second sub-lattice correlithm object2806 b.

The fourth column of table 2820 a illustrates at least a portion of thebit values of the n-dimensional fourth sub-lattice correlithm object2806 d generated by fourth processing stage 2802 d. For each bit of thesecond sub-lattice correlithm object 2806 b that was changed from thefirst sub-lattice correlithm object 2806 a, some of those bit values arechanged from a zero to a one or from a one to a zero, and some of thosebit values are kept the same as in the second sub-lattice correlithmobject 2806 b. For example, in table 2820 b, four bits were changed fromthe first sub-lattice correlithm object 2806 a to the second sub-latticecorrelithm object 2806 b, and of those four bits, one bit value ischanged, and the other three bit values are kept the same as in thesecond sub-lattice correlithm object 2806 b. For each bit of the thirdsub-lattice correlithm object 2806 c that was changed from the firstsub-lattice correlithm object 2806 a, some of those bit values arechanged from a zero to a one or from a one to a zero, and some of thosebit values are kept the same as in the third sub-lattice correlithmobject 2806 c. For example, in table 2820 b, four bits were changed fromthe first sub-lattice correlithm object 2806 a to the third sub-latticecorrelithm object 2806 c, and of those four bits, three bit values arechanged, and the other bit value is kept the same as in the thirdsub-lattice correlithm object 2806 c.

FIG. 30A illustrates one embodiment of a lattice correlithm object 2810with a quadrilateral geometric shape and formed by first sub-latticecorrelithm object 2806 a, second sub-lattice correlithm object 2806 bthat is four bits away from first sub-lattice correlithm object 2806 ain n-dimensional space 102, third sub-lattice correlithm object 2806 cthat is four bits away from first sub-lattice correlithm object 2806 ain n-dimensional space 102, and fourth sub-lattice correlithm object2806 d. Fourth sub-lattice correlithm object 2806 d may be an undefinednumber of bits away from second sub-lattice correlithm object 2806 b andfrom third sub-lattice correlithm object 2806 c. This is because theparticular number and combination of bits that are changed by fourthprocessing stage 2802 d when creating fourth sub-lattice correlithmobject 2806 d from first sub-lattice correlithm object 2806 a, secondsub-lattice correlithm object 2806 b, and third sub-lattice correlithmobject 2806 c can vary.

Square Lattice Correlithm Object

In another embodiment, the fourth processing stage 2802 d may receivethe first sub-lattice correlithm object 2806 a, the second sub-latticecorrelithm object 2806 b, and the third sub-lattice correlithm object2806 c. The fourth processing stage 2802 d outputs a fourth correlithmobject 2806 d where each bit of the fourth sub-lattice correlithm object2806 d has a value that is based on the value of a corresponding bit ofthe first sub-lattice correlithm object 2806 a that was not changed inthe second sub-lattice correlithm object 2806 b by the second processingstage 2802 b and that was not changed in the third sub-latticecorrelithm object 2806 c by the third processing stage 2802 c. Thevalues of the bits of the first sub-lattice correlithm object 2806 athat were changed in the second sub-lattice correlithm object 2806 b andin the third sub-lattice correlithm object 2806 c are further modifiedas follows. For each bit of the second sub-lattice correlithm object2806 b that was changed from the first sub-lattice correlithm object2806 a, the fourth processing stage 2802 d changes from a zero to a oneor from a one to a zero half of those bits and keeps the value the sameas in the second sub-lattice correlithm object 2806 b for the other halfof those bits. Thus, for example, if four bits were changed from thefirst sub-lattice correlithm object 2806 a to the second sub-latticecorrelithm object 2806 b, then two of those four bits are furtherchanged when creating the fourth sub-lattice correlithm object 2806 d,and the other two of those four bits remain the same value. For each bitof the third sub-lattice correlithm object 2806 c that was changed fromthe first sub-lattice correlithm object 2806 a, the fourth processingstage 2802 d changes the value from a zero to a one or from a one to azero for half of those bits and keeps the value the same as in the thirdsub-lattice correlithm object 2806 c for the other half of those bits.Thus, for example, if four bits were changed from the first sub-latticecorrelithm object 2806 a to the third sub-lattice correlithm object 2806b, then two of those four bits are further changed when creating thefourth sub-lattice correlithm object 2806 d, and the other two of thosefour bits remain the same value.

FIG. 29B illustrates a table 2820 b that demonstrates the changes in bitvalues from one sub-lattice correlithm object 2806 to anothersub-lattice correlithm object 2806. For example, the first column oftable 2820 a illustrates at least a portion of the bit values of then-dimensional first sub-lattice correlithm object 2806 a generated byfirst processing stage 2802 a. The second column of table 2820 aillustrates at least a portion of the bit values of the n-dimensionalsecond sub-lattice correlithm object 2806 b generated by secondprocessing stage 2802 b, with four randomly selected bits with valueschanged from a zero to a one, or from a one to a zero. The third columnof table 2820 a illustrates at least a portion of the bit values of then-dimensional third sub-lattice correlithm object 2806 c generated bythird processing stage 2802 c, with another four randomly selected bitswith values changed from a zero to a one, or from a one to a zero. Thebits of the first sub-lattice correlithm object 2806 a that are changedin value to generate the third sub-lattice correlithm object 2806 c maybe selected randomly from the n-bit digital word. However, the bits ofthe first sub-lattice correlithm object 2806 a that are changed in valueto generate the third sub-lattice correlithm object 2802 c are differentfrom the bits of the first sub-lattice correlithm object 2806 a that arechanged in value to generate the second sub-lattice correlithm object2806 b.

The fourth column of table 2820 b illustrates at least a portion of thebit values of the n-dimensional fourth sub-lattice correlithm object2806 d generated by fourth processing stage 2802 d. For each bit of thesecond sub-lattice correlithm object 2806 b that was changed from thefirst sub-lattice correlithm object 2806 a, half of those bit values arechanged from a zero to a one or from a one to a zero, and the other halfof those bit values are kept the same as in the second sub-latticecorrelithm object 2806 b. For example, in table 2820 b, four bits werechanged from the first sub-lattice correlithm object 2806 a to thesecond sub-lattice correlithm object 2806 b, and of those four bits, twobit values are changed, and the other two bit values are kept the sameas in the second sub-lattice correlithm object 2806 b. For each bit ofthe third sub-lattice correlithm object 2806 c that was changed from thefirst sub-lattice correlithm object 2806 a, half of those bit values arechanged from a zero to a one or from a one to a zero, and half of thosebit values are kept the same as in the third sub-lattice latticecorrelithm object 2806 c. For example, in table 2820 b, four bits werechanged from the first sub-lattice correlithm object 2806 a to the thirdsub-lattice correlithm object 2806 c, and of those four bits, two bitvalues are changed, and the other two bit values are kept the same as inthe third sub-lattice correlithm object 2806 c.

FIG. 30B illustrates one embodiment of a lattice correlithm object 2810with a square geometric shape and formed by first sub-lattice correlithmobject 2806 a that is four bits away from each of second sub-latticecorrelithm object 2806 b and third sub-lattice correlithm object 2806 cin n-dimensional space 102, and fourth sub-lattice correlithm object2806 d that is four bits away from each of second sub-lattice correlithmobject 2806 b and third sub-lattice correlithm object 2806 c inn-dimensional space 102. This is because the particular bits that arechanged by fourth processing stage 2802 d when creating fourthsub-lattice correlithm object 2806 d from second sub-lattice correlithmobject 2506 b and third sub-lattice correlithm object 2506 c arespecifically determined as described above.

FIG. 28 further illustrates a node 304 and a node table 2812 stored inmemory 504. Node 304 receives the lattice correlithm object 2810,including each of the sub-lattice correlithm objects 2806, and furtherreceives data elements 2814 in the form of real-world data elements ordata represented by correlithm objects 104. Node table 2812 associatesthe data elements 2814 to the sub-lattice correlithm objects 2806 oflattice correlithm object 2810.

In a particular embodiment, lattice correlithm object 2810 may be usedto represent data elements A, B, C, and D in a non-linear and/ormulti-directional relationship. For example, the degree of relationshipsamong four people may be captured using a lattice correlithm object2810. In particular, a Person A may be represented by sub-latticecorrelithm object 2806 a; Person B may be represented by sub-latticecorrelithm object 2806 b; Person C may be represented by sub-latticecorrelithm object 2806 c, and Person D may be represented by sub-latticecorrelithm object 2806 d. The degree to which Person A knows Person Bmay be represented by the distance in n-dimensional space 102 betweensub-lattice correlithm objects 2806 a and 2806 b. The degree to whichPerson A knows Person C may be represented by the distance inn-dimensional space 102 between sub-lattice correlithm objects 2806 aand 2806 c. The degree to which Person D knows Person B may berepresented by the distance in n-dimensional space 102 betweensub-lattice correlithm objects 2806 d and 2806 b. The degree to whichPerson D knows Person C may be represented by the distance inn-dimensional space 102 between sub-lattice correlithm objects 2806 dand 2806 c. If two people, such as Persons A and B, don't know eachother, then sub-lattice correlithm objects 2806 b and 2806 c may be astandard distance apart from each other in n-dimensional space 102. Therelative degree of relationships between Persons A, B, C, and D can becaptured using a quadrilateral correlithm object 2810, as illustrated inFIGS. 30A and/or 30B.

Moreover, multiple data models may be used to represent asymmetricdegrees of relationships among people from the perspective of specificpeople within that group. For example, two people can know each other todifferent degrees such that a Person A may feel as though he knowsPerson B better than Person B feels they know Person A. The same may betrue for other relationships between and among Persons A, B, C, and D.The asymmetric relationships between the same Persons A, B, C, and D canbe modeled well using correlithm objects by creating one latticecorrelithm object 2810 that models relationships with Persons B, C, andD from Person A's perspective, another lattice correlithm object 2810that models relationships with Persons A, C, and D from Person B'sperspective, another lattice correlithm object 2810 that modelsrelationships with Persons A, B, and D from Person C's perspective, andanother lattice correlithm object 2810 that models relationships withPersons A, B, and C from Person D's perspective.

Although these embodiments were described with respect to therelationships among people, it should be understood that a quadrilaterallattice correlithm object 2810 may be used to represent many differenttypes of relationships between many different types of data elements.

FIG. 31 illustrates one embodiment of an irregular lattice correlithmobject generator 3100 configured to generate an irregular latticecorrelithm object 3110 as output. Irregular lattice correlithm objectgenerator 3100 and its constituent components is implemented by latticecorrelithm object engine 524, and any other suitable elements ofcomputer architecture 500, described above with respect to FIG. 5.Irregular lattice correlithm object generator 3100 comprises a firstprocessing stage 3102 a, a second processing stage 3102 b, a thirdprocessing stage 3102 c, and a fourth processing stage 3102 dcommunicatively coupled to each other. First processing stage 3102 areceives an input 3104 and outputs a first sub-lattice correlithm object3106 a that comprises an n-bit digital word wherein each bit has eithera value of zero or one. In one embodiment, first processing stage 3102 agenerates the values of each bit randomly. Input 3104 comprises one ormore parameters used to determine the characteristics of the latticecorrelithm object 3110. For example, input 3104 may include a parameterfor the number of dimensions, n, in the n-dimensional space 102 (e.g.,64, 128, 256, etc.) in which to generate the lattice correlithm object3110. Input 3104 may also include a distance parameter, δ, thatindicates a particular number of bits of the n-bit digital word (e.g.,4, 8, 16, etc.) that will be changed from one sub-lattice correlithmobject 3106 to the next in the lattice correlithm object 3110. AlthoughFIG. 31 illustrates only first processing stage 3102 a receiving input3104, it should be understood that second processing stage 3102 b, thirdprocessing stage 3102 c, and fourth processing stage 3102 d may alsoreceive input 2504 (or at least the information contained within input2504).

In the examples described below, assume that n=64 such that eachsub-lattice correlithm object 3106 of the lattice correlithm object 3110is a 64-bit digital word. As discussed previously with regard to FIG. 9,the standard deviation is equal to

$\sqrt{\frac{n}{4}},$

or four bits, for a 64-dimensional space 102. In one embodiment, thedistance parameter, δ, is selected to equal the standard deviation. Inthis embodiment, the distance parameter is also four bits (e.g., onestandard deviation). Although in the examples described below, theselected distance parameter is four bits, other distance parameters maybe used in input 3104 depending on the particular structure andarrangement desired for irregular lattice correlithm object 3110. Ingeneral, the fewer the number of bits that are changed betweensub-lattice correlithm objects 3106, the tighter the correlation betweenthose sub-lattice correlithm objects 3106; and the greater the number ofbits that are changed between sub-lattice correlithm objects 3106, thelooser the correlation between those sub-lattice correlithm objects3106. Moreover, it is not necessary that each processing stage 3102 ofirregular correlithm object generator 3100 strictly change the exactnumber of bits identified by the distance parameter from one sub-latticecorrelithm object 3106 to the next. Instead, the present disclosurecontemplates some variance from the number of bits indicated by thedistance parameter (e.g., a fewer or a greater number of bits beingchanged).

Second processing stage 3102 b receives the first sub-lattice correlithmobject 3106 a and generates a plurality of first cluster sub-latticecorrelithm objects 3108 a. FIG. 32 illustrates one embodiment of anirregular lattice correlithm object 3110 that comprises firstsub-lattice correlithm object 3106 a with a plurality of first clustersub-lattice correlithm objects 3108 a surrounding it in an n-dimensionalsphere. Referring back to FIG. 31, each of the first cluster sub-latticecorrelithm objects 3108 a comprises an n-bit digital word, wherein eachbit of each of the first cluster sub-lattice correlithm objects 3108 ahas a value that is based on a value of a corresponding bit of the firstsub-lattice correlithm object 3106 a and changed values for a particularnumber of bits identified by the distance parameter, δ. For example, fora plurality of bits of the first sub-lattice correlithm object 3106 a upto the particular number of bits identified in the distance parameter,δ, the second processing stage 3102 b changes the value from a zero to aone or from a one to a zero to generate each of the first clustersub-lattice correlithm objects 3108 a. In this example, four bit valuesare changed from the first sub-lattice correlithm object 3106 a togenerate each of the first cluster sub-lattice correlithm objects 3108a. The bits of the first sub-lattice correlithm object 3106 a that arechanged in value to generate each of the first cluster sub-latticecorrelithm objects 3108 a may be different and selected randomly fromthe n-bit digital word. The other bits of the n-bit digital word in eachof the first cluster sub-lattice correlithm objects 3108 a remain thesame values as the corresponding bits of the first sub-latticecorrelithm object 3106 a. By changing four bit values, the core of firstsub-lattice correlithm object 3106 a overlaps in n-dimensional spacewith the core of each first cluster sub-lattice correlithm object 3108a.

Third processing stage 3102 c receives one of the first clustersub-lattice correlithm objects 3108 a as a second sub-lattice correlithmobject 3106 b. The particular first cluster sub-lattice correlithmobject 3108 a received by third processing stage 3102 c may bedetermined randomly by one or more components of irregular correlithmobject generator 3100. Third processing stage 3102 uses the secondsub-lattice correlithm object 3106 b to generate a plurality of secondcluster sub-lattice correlithm objects 3108 b. FIG. 32 illustrates thatone of the first cluster sub-lattice correlithm objects 3108 a isselected (e.g., randomly) to be second sub-lattice correlithm object3106 b. Furthermore, FIG. 32 illustrates that irregular latticecorrelithm object 3110 comprises second sub-lattice correlithm object3106 b with a plurality of second cluster sub-lattice correlithm objects3108 b surrounding it in an n-dimensional sphere. Referring back to FIG.31, each of the second cluster sub-lattice correlithm objects 3108 bcomprises an n-bit digital word, wherein each bit of each of the secondcluster sub-lattice correlithm objects 3108 b has a value that is basedon a value of a corresponding bit of the second sub-lattice correlithmobject 3106 b and changed values for a particular number of bitsidentified by the distance parameter, δ. For example, for a plurality ofbits of the second sub-lattice correlithm object 3106 bup to theparticular number of bits identified in the distance parameter, δ, thethird processing stage 3102 c changes the value from a zero to a one orfrom a one to a zero to generate each of the second cluster sub-latticecorrelithm objects 3108 b. In this example, four bit values are changedfrom the second sub-lattice correlithm object 3106 b to generate each ofthe second cluster sub-lattice correlithm objects 3108 b. The bits ofthe second sub-lattice correlithm object 3106 b that are changed invalue to generate each of the second cluster sub-lattice correlithmobjects 3108 b may be different and selected randomly from the n-bitdigital word. The other bits of the n-bit digital word in each of thesecond cluster sub-lattice correlithm objects 3108 b remain the samevalues as the corresponding bits of the second sub-lattice correlithmobject 3106 b. By changing four bit values, the core of secondsub-lattice correlithm object 3106 b overlaps in n-dimensional spacewith the core of each second cluster sub-lattice correlithm object 3108b.

Fourth processing stage 3102 d receives one of the second clustersub-lattice correlithm objects 3108 b as a third sub-lattice correlithmobject 3106 c. The particular second cluster sub-lattice correlithmobject 3108 b received by fourth processing stage 3102 d may bedetermined randomly by one or more components of irregular correlithmobject generator 3100. Fourth processing stage 3102 uses the thirdsub-lattice correlithm object 3106 c to generate a plurality of thirdcluster sub-lattice correlithm objects 3108 c. FIG. 32 illustrates thatone of the second cluster sub-lattice correlithm objects 3108 b isselected (e.g., randomly) to be third sub-lattice correlithm object 3106c. Furthermore, FIG. 32 illustrates that irregular lattice correlithmobject 3110 comprises third sub-lattice correlithm object 3106 c with aplurality of third cluster sub-lattice correlithm objects 3108 csurrounding it in an n-dimensional sphere. Referring back to FIG. 31,each of the third cluster sub-lattice correlithm objects 3108 ccomprises an n-bit digital word, wherein each bit of each of the thirdcluster sub-lattice correlithm objects 3108 c has a value that is basedon a value of a corresponding bit of the third sub-lattice correlithmobject 3106 c and changed values for a particular number of bitsidentified by the distance parameter, δ. For example, for a plurality ofbits of the third sub-lattice correlithm object 3106 c up to theparticular number of bits identified in the distance parameter, δ, thefourth processing stage 3102 d changes the value from a zero to a one orfrom a one to a zero to generate each of the third cluster sub-latticecorrelithm objects 3108 c. In this example, four bit values are changedfrom the third sub-lattice correlithm object 3106 c to generate each ofthe third cluster sub-lattice correlithm objects 3108 c. The bits of thethird sub-lattice correlithm object 3106 c that are changed in value togenerate each of the third cluster sub-lattice correlithm objects 3108 cmay be different and selected randomly from the n-bit digital word. Theother bits of the n-bit digital word in each of the third clustersub-lattice correlithm objects 3108 c remain the same values as thecorresponding bits of the third sub-lattice correlithm object 3106 c. Bychanging four bit values, the core of third sub-lattice correlithmobject 3106 c overlaps in n-dimensional space with the core of eachthird cluster sub-lattice correlithm object 3108 c.

Irregular lattice correlithm object generator 3100 may include anysuitable number of additional processing stages 3102 to generateadditional sub-lattice correlithm objects 3106, each with a plurality ofcluster correlithm objects 3108 surrounding it in an n-dimensionalsphere.

FIG. 31 further illustrates a node 304 and node table 3112 stored inmemory 504. Node 304 receives the first sub-lattice correlithm object3106 a and first cluster correlithm objects 3108 a, second sub-latticecorrelithm object 3106 b and second cluster correlithm objects 3108 b,third sub-lattice correlithm object 3106 c and third cluster correlithmobjects 3108 c, and so on. Node 304 further receives data elements 3114in the form of real-world data or data represented by other correlithmobjects 104 that are mapped to one or more sub-lattice correlithmobjects 3106 a-c and/or cluster correlithm objects 3108 a-c in nodetable 3112.

In a particular embodiment, irregular lattice correlithm object 3110 maybe used to represent multiple data elements that are clustered around acentral data element in an n-dimensional sphere. Irregular latticecorrelithm object 3110 may further be used to represent multiplen-dimensional spheres that are related to each other. For example,Person A may be represented by a sub-lattice correlithm object 3106 a.Person A's friends may be represented by a plurality of clustercorrelithm objects 3108 a that surround sub-lattice correlithm object3106 a in an n-dimensional sphere. One of Person A's friends, Person B,may then be represented by a sub-lattice correlithm object 3106 b andPerson B's friends may be represented by a plurality of clustercorrelithm objects 3108 b. Similarly, one of Person B's friends, PersonC, may then be represented by a sub-lattice correlithm object 3106 c andPerson C's friends may be represented by a plurality of clustercorrelithm objects 3108 c, and so on. In this way, the relationships ofa large group of people may be captured using irregular latticecorrelithm objects 3110. Although this embodiment was described withrespect to the relationships among people, it should be understood thatan irregular lattice correlithm object 3110 may be used to representmany different types of relationships between many different types ofdata elements.

FIG. 32A illustrates one embodiment of a bidirectional string correlithmobject generator 3200 configured to generate a bidirectional stringcorrelithm object 3210 as output. Bidirectional string correlithm objectgenerator 3200 and its constituent components is implemented by stringcorrelithm object engine 522, and any other suitable elements ofcomputer architecture 500, described above with respect to FIG. 5.Bidirectional string correlithm object generator 3200 comprises a firstprocessing stage 3202 a communicatively and logically coupled to asecond processing stage 3202 b and a third processing stage 3202 c.First processing stage 3202 a receives an input 3204 and outputs a firstsub-string correlithm object 3206 a that comprises an n-bit digital wordwherein each bit has either a value of zero or one. In one embodiment,first processing stage 3202 a generates the values of each bit randomly.First sub-string correlithm object 3206 a may also be referred to as acentral sub-string correlithm object 3206 a. Input 3204 comprises one ormore parameters used to determine the characteristics of thebidirectional string correlithm object 3210. For example, input 3204 mayinclude a parameter for the number of dimensions, n, in then-dimensional space 102 (e.g., 64, 128, 256, etc.) in which to generatethe bidirectional string correlithm object 3210. Input 3204 may alsoinclude a distance parameter, δ, that indicates a particular number ofbits of the n-bit digital word (e.g., 4, 8, 16, etc.) that will bechanged from one sub-string correlithm object 3206 to the next in thebidirectional string correlithm object 3210. Although FIG. 32Aillustrates only first processing stage 3202 a receiving input 3204, itshould be understood that both second processing stage 3202 b and thirdprocessing stage 3202 c may also receive input 3204 (or at least theinformation contained within input 3204).

In the examples described below, assume that n=64 such that eachsub-string correlithm object 3206 of the string correlithm object 3210is a 64-bit digital word. As discussed previously with regard to FIG. 9,the standard deviation is equal to

$\sqrt{\frac{n}{4}},$

or four bits, for a 64-dimensional space 102. In one embodiment, thedistance parameter, δ, is selected to equal the standard deviation. Inthis embodiment, the distance parameter is also four bits (e.g., onestandard deviation). Although in the examples described below, theselected distance parameter is four bits, other distance parameters maybe used in input 3204 depending on the particular structure andarrangement desired for bidirectional string correlithm object 3210. Ingeneral, the fewer the number of bits that are changed betweensub-string correlithm objects 3206, the tighter the correlation betweenthose sub-string correlithm objects 3206; and the greater the number ofbits that are changed between sub-string correlithm objects 3206, thelooser the correlation between those sub-string correlithm objects 3206.Moreover, it is not necessary that each processing stage 3202 ofbidirectional correlithm object generator 3200 strictly change the exactnumber of bits identified by the distance parameter from one sub-stringcorrelithm object 3206 to the next. Instead, the present disclosurecontemplates some variance from the number of bits indicated by thedistance parameter (e.g., a fewer or a greater number of bits beingchanged).

Second processing stage 3202 b receives the first sub-string correlithmobject 3206 a and, for each bit of the first sub-string correlithmobject 3206 a up to the particular number of bits identified in thedistance parameter, δ, changes the value from a zero to a one or from aone to a zero to generate a second sub-string correlithm object 3206 b.The bits of the first sub-string correlithm object 3206 a that arechanged in value for the second sub-string correlithm object 3206 b areselected randomly from the n-bit digital word. The other bits of then-bit digital word in second sub-string correlithm object 3206 b remainthe same values as the corresponding bits of the first sub-stringcorrelithm object 3206 a.

Third processing stage 3202 c also receives the first sub-stringcorrelithm object 3206 a and, for each bit of the first sub-stringcorrelithm object 3206 a up to the particular number of bits identifiedin the distance parameter, δ, changes the value from a zero to a one orfrom a one to a zero to generate a third sub-string correlithm object3206 c. The bits of the first sub-string correlithm object 3206 a thatare changed in value for the third sub-string correlithm object 3206 care selected randomly from the n-bit digital word. The other bits of then-bit digital word in third sub-string correlithm object 3206 c remainthe same values as the corresponding bits of the first sub-stringcorrelithm object 3206 a. Both the second sub-string correlithm object3206 b and the third sub-string correlithm object 3206 c extend from thefirst sub-string correlithm object 3206 a, also referred to as thecentral sub-string correlithm object 3206 a, but in differentn-dimensional directions. Thus, the resulting string correlithm object3210 is referred to as a bidirectional string correlithm object 3210.

FIG. 32B illustrates a table 3220 that demonstrates the changes in bitvalues from a first sub-string correlithm object 3206 a to a secondsub-string correlithm object 3206 b in one n-dimensional direction, andfrom first sub-string correlithm object 3206 a to third sub-stringcorrelithm object 3206 c in another n-dimensional direction. In thisexample, assume that n=64 such that each sub-string correlithm object3206 of the bidirectional string correlithm object 3210 is a 64-bitdigital word. As discussed previously with regard to FIG. 9, thestandard deviation is equal to

$\sqrt{\frac{n}{4}},$

or four bits, for a 64-dimensional space 102. In one embodiment, thedistance parameter, δ, is selected to equal the standard deviation. Inthis embodiment, the distance parameter is also four bits which meansthat four bits will be changed from each sub-string correlithm object3206 to the next in the bidirectional string correlithm object 3210. Inother embodiments where it is desired to create a tighter correlationamong sub-string correlithm objects 3206, a distance parameter may beselected to be less than the standard deviation (e.g., distanceparameter of three bits or less where standard deviation is four bits).In still other embodiments where it is desired to create a loosercorrelation among sub-string correlithm objects 3206, a distanceparameter may be selected to be more than the standard deviation (e.g.,distance parameter of five bits or more where standard deviation is fourbits).

Table 3220 illustrates the first sub-string correlithm object 3206 a inthe middle column having four bit values that are changed, by secondprocessing stage 3202 b, from a zero to a one or from a one to a zero togenerate second sub-string correlithm object 3206 b in one n-dimensionaldirection. By changing four bit values, the core of the first sub-stringcorrelithm object 3206 a overlaps in 64-dimensional space with the coreof the second sub-string correlithm object 3206 b. Table 3220 alsoillustrates the first sub-string correlithm object 3206 a having fourbit values that are changed, by third processing stage 3202 c, from azero to a one or from a one to a zero to generate third sub-stringcorrelithm object 3206 c in another n-dimensional direction. By changingfour bit values, the core of the first sub-string correlithm object 3206a overlaps in 64-dimensional space with the core of the third sub-stringcorrelithm object 3206 c.

Referring back to FIG. 32A, the second processing stage 3202 b receivesfrom itself the second sub-string correlithm object 3206 b as feedback.For each bit of the second sub-string correlithm object 3206 b up to theparticular number of bits identified by the distance parameter, thesecond processing stage 3202 b changes the value from a zero to a one orfrom a one to a zero to generate a fourth sub-string correlithm object3206 d. The bits of the second sub-string correlithm object 3206 b thatare changed in value for the fourth sub-string correlithm object 3206 dare selected randomly from the n-bit digital word. The other bits of then-bit digital word in fourth sub-string correlithm object 3206 d remainthe same values as the corresponding bits of the second sub-stringcorrelithm object 3206 b. Referring to table 3220 illustrated in FIG.32B, the second sub-string correlithm object 3206 b has four bit valuesthat are changed, by second processing stage 3202 b, from a zero to aone or from a one to a zero to generate fourth sub-string correlithmobject 3206 d.

Referring back to FIG. 32A, the third processing stage 3202 c receivesfrom itself the third sub-string correlithm object 3206 c as feedback.For each bit of the third sub-string correlithm object 3206 c up to theparticular number of bits identified by the distance parameter, thethird processing stage 3202 c changes the value from a zero to a one orfrom a one to a zero to generate a fifth sub-string correlithm object3206 e. The bits of the third sub-string correlithm object 3206 c thatare changed in value for the fifth sub-string correlithm object 3206 eare selected randomly from the n-bit digital word. The other bits of then-bit digital word in fifth sub-string correlithm object 3206 e remainthe same values as the corresponding bits of the third sub-stringcorrelithm object 3206 c. Referring to table 3220 illustrated in FIG.32B, the third sub-string correlithm object 3206 c has four bit valuesthat are changed, by third processing stage 3202 c, from a zero to a oneor from a one to a zero to generate fifth sub-string correlithm object3206 e.

Referring back to FIG. 32A, each of the second processing stage 3202 band third processing stage 3202 c may successively output a subsequentsub-string correlithm object 3206 by changing bit values of theimmediately prior sub-string correlithm object 3206 received asfeedback, as described above. This process continues for a predeterminednumber of sub-string correlithm objects 3206 in the bidirectional stringcorrelithm object 3210. Together, the sub-string correlithm objects 3206form a bidirectional string correlthim object 3210 in which the firstsub-string correlithm object 3206 a is in the middle and the adjacentsub-string correlithm objects 3206 extend from first sub-stringcorrelithm object 3206 a in different n-dimensional directions. In oneembodiment, each sub-string correlithm object 3206 is separated from anadjacent sub-string correlithm object 3206 in n-dimensional space 102 bya number of bits represented by the distance parameter, δ.

FIG. 33A illustrates one embodiment of bidirectional string correlithmobject 3210 that includes sub-string correlithm objects 3206 that extendin different n-dimensional directions from a central sub-stringcorrelithm object 3206 a. In particular, sub-string correlithm objects3206 b and 3206 d extend in one n-dimensional direction from centralsub-string correlithm object 3206 a, and sub-string correlithm objects3206 c and 3206 e extend in a different n-dimensional direction fromcentral sub-string correlithm object 3206 a. Although FIG. 33Aillustrates bidirectional string correlithm object 3210 as a lineargeometric shape, it should be understood that bidirectional stringcorrelithm object 3210 need not be linear or even planar in geometry.Instead, pairs of sub-string correlithm objects 3206 of bidirectionalstring correlithm object 3210 may reside in different planes from eachother in n-dimensional space 102. Moreover, the path between groups ofsub-string correlithm objects 3206 of bidirectional string correlithmobject 3210 may be non-linear in n-dimensional space 102.

FIG. 32A further illustrates a node 304 and a node table 3212 stored inmemory 504. Node 304 receives the lattice correlithm object 3210,including each of the sub-lattice correlithm objects 3206, and furtherreceives data elements 3214 in the form of real-world data elements ordata represented by correlithm objects 104. Node table 3212 associatesthe data elements 3214 to the sub-lattice correlithm objects 3206 oflattice correlithm object 3210.

FIG. 33B illustrates a special case of a bidirectional string correlithmobject 3210 referred to as a multiple string correlithm object 3230.Similar to a bidirectional string correlithm object 3210, a multiplestring correlithm object 3230 also has a central sub-string correlithmobject 3206 a from which other sub-string correlithm objects 3206 extendin different n-dimensional directions. Thus, in essence, a multiplestring correlithm object 3230 includes multiple bidirectional stringcorrelithm objects that extend in different n-dimensional directionsfrom a common central sub-string correlithm object 3206 a. Accordingly,a multiple string correlithm object 3230 has multiple string correlithmobjects that intersect at the central correlithm object 3206 a. Inparticular, as illustrated in FIG. 33B, multiple string correlithmobject 3230 is similar to bidirectional sub-string correlithm object3210 in that it has sub-string correlithm objects 3206 b and 3206 dextending in one n-dimensional direction from central sub-stringcorrelithm object 3206 a, and other sub-string correlithm objects 3206 cand 3206 e extending in another n-dimensional direction from centralsub-string correlithm object 3206 a. In addition, multiple stringcorrelithm object 3230 has any number and combination of additionalstring correlithm objects that intersect at central sub-stringcorrelithm object 3206 a and extend in different n-dimensionaldirections. In particular, multiple string correlithm object 3230 hasanother string correlithm object that includes sub-string correlithmobjects 3206 f and 3206 h extending in one n-dimensional direction fromcentral sub-string correlithm object 3206 a, and sub-string correlithmobjects 3206 g and 3206 i extending in another n-dimensional directionfrom central sub-string correlithm object 3206 a. Furthermore, multiplestring correlithm object 3230 has still another string correlithm objectthat includes sub-string correlithm objects 3206 j and 3206 l extendingin one n-dimensional direction from central sub-string correlithm object3206 a, and sub-string correlithm objects 3206 k and 3206 m extending inanother n-dimensional direction from central sub-string correlithmobject 3206 a.

As with bidirectional string correlithm object 3210, it should beunderstood that multi-directional string correlithm object 3230 need notbe linear or even planar in geometry. Instead, pairs of sub-stringcorrelithm objects 3206 of multi-directional string correlithm object3230 may reside in different planes from each other in n-dimensionalspace. Similarly, different string correlithm objects of multiple stringcorrelithm object 3230 may reside in different planes from each other inn-dimensional space. Moreover, the path between groups of sub-stringcorrelithm objects 3206 of multi-directional string correlithm object3210 may be non-linear in n-dimensional space 102.

In one embodiment, bidirectional string correlithm object generator 3200may be used to generate multiple string correlithm object 3230. Inparticular, referring back to FIG. 32A, first processing stage 3202 amay be used to generate first sub-string correlithm object 3206 a;second processing stage 3202 b may be used to generate second sub-stringcorrelithm object 3206 b and fourth sub-string correlithm object 3206 d;and third processing stage 3202 c may be used to generate thirdsub-string correlithm 3206 c and fifth sub-string correlithm object 3206e, all as described above with respect to FIG. 32A. From this point,however, first sub-string correlithm object 3206 a may be used togenerate another string correlithm object. This is performed by againfeeding first sub-string correlithm object 3206 a to second processingstage 3202 b and third processing stage 3202 c which generate sub-stringcorrelithm objects 3206 f and 3206 g, respectively, using the techniquesdescribed above with respect to FIG. 32A. Those sub-string correlithmobjects 3206 f and 3206 g can themselves be fed back to secondprocessing stage 3202 b and third processing stage 3202 c to generatesub-string correlithm objects 3206 h and 3206 i, respectively, alsousing the techniques described above with respect to FIG. 32A. Thissecond bidirectional string correlithm object 3210 intersects with thefirst bidirectional string correlithm object 3210 at the centralsub-string correlithm object 3206 a.

Bidirectional string correlithm object generator 3200 may be used togenerate any number and combination of additional bidirectional stringcorrelithm objects 3210 that also intersect at central sub-stringcorrelithm object 3206 a. For example, first sub-string correlithmobject 3206 a can again be fed to second processing stage 3202 b andthird processing stage 3202 c which generate sub-string correlithmobjects 3206 j and 3206 k, respectively, using the techniques describedabove with respect to FIG. 32A. Those sub-string correlithm objects 3206j and 3206 k can themselves be fed back to second processing stage 3202b and third processing stage 3202 c to generate sub-string correlithmobjects 3206 l and 3206 m, respectively, also using the techniquesdescribed above with respect to FIG. 32A. This third bidirectionalstring correlithm object 3210 intersects with the first and secondbidirectional string correlithm objects 3210 at the central sub-stringcorrelithm object 3206 a.

In a particular embodiment, bidirectional string correlithm object 3210may be used to represent data elements A, B, and C having non-linearand/or multi-directional relationships with each other. For example, aPerson A may be related to a Person B that is a parent and to a Person Cthat is a child. Such a multi-directional relationship may be capturedby a bidirectional string correlithm object 3210 where Person A isrepresented by a central sub-string correlithm object 3206 a, Person Bmay be represented by sub-string correlithm object 3206 b, and Person Cmay be represented by sub-string correlithm object 3206 c, asillustrated in FIG. 33A. Moreover, a Person A may have many suchmulti-directional relationships that can be represented using multiplebidirectional string correlithm objects 3210 that each intersect at aPerson A that is represented by a sub-string correlithm object 3206 a,as illustrated in FIG. 33B. Although these embodiments were describedwith respect to the relationships between people, it should beunderstood that a bidirectional string correlithm object 3210 may beused to represent many different types of relationships between manydifferent types of data elements.

FIG. 34 is a flowchart of an embodiment of a process 3400 for generatinga bidirectional string correlithm object 3210 and/or a multiple stringcorrelithm object 3230. At step 3402, a first sub-string correlithmobject 3206 a is generated, such as by a first processing stage 3202 aof a bidirectional string correlithm object generator 3200. The firstsub-string correlithm object 3206 a comprises an n-bit digital word. Atstep 3404, the first sub-string correlithm object 3206 a is communicatedto the second processing stage 3202 b in conjunction with extending thebidirectional string correlithm object 3210 in a first n-dimensionaldirection, as indicated by path 3406, and to a third processing stage3202 c in conjunction with extending the bidirectional string correlithmobject 3210 in a second n-dimensional direction, as indicated by path3408.

Referring to path 3406, execution proceeds to step 3410 where a bit ofthe n-bit digital word of the received sub-string correlithm object 3206is randomly selected, and is changed at step 3412 from a zero to a oneor from a one to a zero. Execution proceeds to step 3414 where it isdetermined whether to change additional bits in the n-bit digital word.In general, process 3400 will change a particular number of bits up tothe distance parameter, δ. In one embodiment, as described above withregard to FIGS. 32A-B, the distance parameter is four bits. Ifadditional bits remain to be changed in the sub-string correlithm object3206, then execution returns to step 3410. If all of the bits up to theparticular number of bits in the distance parameter have already beenchanged, as determined at step 3414, then execution proceeds to step3416 where the next sub-string correlithm object 3206 is output. Theother bits of the n-bit digital word in that next sub-string correlithmobject 3206 which is output at step 3416 remain the same values as thecorresponding bits of the previous sub-string correlithm object 3206.

Execution proceeds to step 3418 where it is determined whether togenerate additional sub-string correlithm objects 3206 of thebidirectional string correlithm object 3210 in the first n-dimensionaldirection. If so, execution proceeds to step 3419 where the newlycreated sub-string correlithm object 3406 is communicated as feedback tothe second processing stage 3202 b. Thereafter, execution returns backto step 3410 of path 3406 and the remainder of the process occurs againto change particular bits up to the number of bits in the distanceparameter, δ. Each subsequent sub-string correlithm object 3206 isseparated from the immediately preceding sub-string correlithm object3206 in n-dimensional space 102 by a number of bits represented by thedistance parameter, δ. If no more sub-string correlithm objects 3206 areto be generated for the bidirectional string correlithm object 3210 inthe first n-dimensional direction in conjunction with path 3406, asdetermined at step 3418, then execution of process 3400 proceeds to step3430.

Referring to path 3408, execution proceeds to step 3420 where a bit ofthe n-bit digital word of the received sub-string correlithm object 3206is randomly selected, and is changed at step 3422 from a zero to a oneor from a one to a zero. Execution proceeds to step 3424 where it isdetermined whether to change additional bits in the n-bit digital word.In general, process 3400 will change a particular number of bits up tothe distance parameter, δ. In one embodiment, as described above withregard to FIGS. 32A-B, the distance parameter is four bits. Ifadditional bits remain to be changed in the sub-string correlithm object3206, then execution returns to step 3420. If all of the bits up to theparticular number of bits in the distance parameter have already beenchanged, as determined at step 3424, then execution proceeds to step3426 where the next sub-string correlithm object 3206 is output. Theother bits of the n-bit digital word in that next sub-string correlithmobject 3206 which is output at step 3426 remain the same values as thecorresponding bits of the previous sub-string correlithm object 3206.

Execution proceeds to step 3428 where it is determined whether togenerate additional sub-string correlithm objects 3206 of thebidirectional string correlithm object 3210 in the second n-dimensionaldirection. If so, execution proceeds to step 3429 wherein the newlycreated sub-string correlithm object 3406 is communicated as feedback tothe third processing stage 3202 c. Thereafter, execution returns back tostep 3420 of path 3408 and the remainder of the process occurs again tochange particular bits up to the number of bits in the distanceparameter, δ. Each subsequent sub-string correlithm object 3206 isseparated from the immediately preceding sub-string correlithm object3206 in n-dimensional space 102 by a number of bits represented by thedistance parameter, δ. If no more sub-string correlithm objects 3206 areto be generated for the bidirectional string correlithm object 3210 inthe second n-dimensional direction in conjunction with path 3408, asdetermined at step 3428, then execution of process 3400 proceeds to step3430.

At step 3430, it is determined whether to generate additionalbidirectional string correlithm objects, such as to form a multiplestring correlithm object 3230 with a common central sub-stringcorrelithm object 3206 a. If so, then execution returns to step 3404where the first sub-string correlithm object 3206 a is againcommunicated to a second processing stage 3202 b in conjunction withfollowing a first path 3406, and further communicated to a thirdprocessing stage 3202 c in conjunction with following a second path3408. Execution of the steps associated with paths 3406 and 3408 proceedas described above. Any number and combination of additionalbidirectional string correlithm objects 3210 may be formed as a part ofa multiple string correlithm object 3230 based on the outcome of thedecision step 3430. If no further bidirectional string correlithmobjects 3210 are to be formed as part of the multiple string correlithmobject 3230, as determined at decision step 3430, then executionterminates at step 3432.

FIG. 35 illustrates how a first string correlithm object 602 a can belinked to a second string correlithm object 602 b. The first stringcorrelithm object 602 a includes a first plurality of sub-stringcorrelithm objects 1206 a-e. For ease of reference, first stringcorrelithm object 602 a is referred to as “S1” and first plurality ofsub-string correlithm objects 1206 a-e are referred to as “S1-1”,“S1-2”, “S1-3”, “S1-4”, and “S1-5”. Second string correlithm object 602b includes a second plurality of sub-string correlithm objects 1206 f-k.For ease of reference, second string correlithm object 602 b is referredto as “S2” and second plurality of sub-string correlithm objects 1206f-k are referred to as “S2-1”, “S2-2”, “S2-3”, “S2-4”, and “S2-5”, and“S2-6”. The sub-string correlithm objects 1206 a-e and 1206 f-k embodyan ordering, sequencing, and distance relationships to each other inn-dimensional space 102.

In one embodiment, sub-string correlithm objects 1206 a-e of firststring correlithm object 602 a and sub-string correlithm objects 1206f-k of second string correlithm object 602 b can each be represented bya digital word of one length, n. In another embodiment, sub-stringcorrelithm objects 1206 a-e of first string correlithm object 602 a caneach be represented by a digital word of one length, n, and sub-stringcorrelithm objects 1206 f-k of second string correlithm object 602 b caneach be represented by a digital word of a different length, m. Each ofthe second plurality of sub-string correlithm objects 1206 f-k areunrelated to each of the first plurality of sub-string correlithmobjects 1206 a-e, in n-dimensional space 102. For example, in aparticular embodiment, none of the first plurality of sub-stringcorrelithm objects 1206 a-e has a bit value in common with any of thesecond plurality of sub-string correlithm objects 1206 f-k.

A node 304 receives first string correlithm object 602 a and secondstring correlithm object 602 b and links at least one sub-stringcorrelithm object 1206 a-e with at least one sub-string correlithmobject 1206 f-k. Because node 304 links together one sub-stringcorrelithm object 1206 with another, it may be referred to as a “link”node 304. A node table 3500 a stored in memory 504 associates at leastone of the first plurality of sub-string correlithm objects 1206 a-ewith at least one of the second plurality of sub-string correlithmobjects 1206 f-k. For example, as illustrated in FIG. 35, node table3500 associates sub-string correlithm object 1206 d, also referred to as“S1-4”, of first string correlithm object 602 a with sub-stringcorrelithm object 1206 h, also referred to as “S2-3”, of second stringcorrelithm object 602 b.

FIG. 35 further illustrates how a first lattice correlithm object 2510can be linked to a second lattice correlithm object 2810. The firstlattice correlithm object 2510 includes a first plurality of sub-stringcorrelithm objects 2506 a-c. For ease of reference, first latticecorrelithm object 2510 is referred to as “L1” and first plurality ofsub-lattice correlithm objects 2506 a-c are referred to as “L1-1”,“L1-2”, and “L1-3”. Second lattice correlithm object 2810 includes asecond plurality of sub-lattice correlithm objects 2806 a-d. For ease ofreference, second lattice correlithm object 2810 is referred to as “L2”and second plurality of sub-lattice correlithm objects 2806 a-d arereferred to as “L2-1”, “L2-2”, “L2-3”, and “L2-4”. The sub-latticecorrelithm objects 2506 a-c and 2806 a-d embody an ordering, sequencing,and distance relationships to each other in n-dimensional space 102.

In one embodiment, sub-lattice correlithm objects 2506 a-c of firstlattice correlithm object 2510 and sub-string correlithm objects 2806a-d of second lattice correlithm object 2810 can each be represented bya digital word of one length, n. In another embodiment, sub-latticecorrelithm objects 2506 a-c of first lattice correlithm object 2510 caneach be represented by a digital word of one length, n, and sub-latticecorrelithm objects 2806 a-d of second lattice correlithm object 2810 caneach be represented by a digital word of a different length, m. Each ofthe second plurality of sub-lattice correlithm objects 2806 a-d areunrelated to each of the first plurality of sub-lattice correlithmobjects 2506 a-c, in n-dimensional space 102. For example, in aparticular embodiment, none of the first plurality of sub-latticecorrelithm objects 2506 a-c has a bit value in common with any of thesecond plurality of sub-lattice correlithm objects 2806 a-d.

A node 304 receives first lattice correlithm object 2510 and secondlattice correlithm object 2810 and links at least one sub-latticecorrelithm object 2506 a-c with at least one sub-lattice correlithmobject 2806 a-d. A node table 3500 b stored in memory 504 associates atleast one of the first plurality of sub-lattice correlithm objects 2506a-c with at least one of the second plurality of sub-lattice correlithmobjects 2806 a-d. For example, as illustrated in FIG. 35, node table3500 b associates sub-lattice correlithm object 2506 c, also referred toas “L1-3”, of first lattice correlithm object 2510 with sub-latticecorrelithm object 2806 a, also referred to as “L2-1”, of second latticecorrelithm object 2810.

A node 304 may also link together a string correlithm object 602 with alattice correlithm object 2510 or 2810. In this embodiment, none of thesub-string correlithm objects 1206 of the string correlithm object 602has a bit value in common with any of the sub-lattice correlithm objects2506 or 2806 of lattice correlithm objects 2510 or 2810. For example, asillustrated in FIG. 35, a node table 3500 c stored in memory 504associates sub-string correlithm object 1206 f of second sub-stringcorrelithm object 602 b, also referred to as “S2-1”, with sub-latticecorrelithm object 2506 a of lattice correlithm object 2510, alsoreferred to as “L1-1”.

Although the description above is detailed with respect to linkingstring correlithm objects 602, lattice correlithm objects 2510, andlattice correlithm objects 2810 among and between each other, it shouldbe understood that a link node 3400 may be used to link any combinationof string correlithm objects 602, bidirectional string correlithmobjects 3210, multiple intersect string correlithm objects 3230,triangle lattice correlithm objects 2510, hexagonal lattice correlithmobjects 2512, quadrilateral lattice correlithm objects 2810, andirregular lattice correlithm objects 3110 among and between each other.

While several embodiments have been provided in the present disclosure,it should be understood that the disclosed systems and methods might beembodied in many other specific forms without departing from the spiritor scope of the present disclosure. The present examples are to beconsidered as illustrative and not restrictive, and the intention is notto be limited to the details given herein. For example, the variouselements or components may be combined or integrated in another systemor certain features may be omitted, or not implemented.

In addition, techniques, systems, subsystems, and methods described andillustrated in the various embodiments as discrete or separate may becombined or integrated with other systems, modules, techniques, ormethods without departing from the scope of the present disclosure.Other items shown or discussed as coupled or directly coupled orcommunicating with each other may be indirectly coupled or communicatingthrough some interface, device, or intermediate component whetherelectrically, mechanically, or otherwise. Other examples of changes,substitutions, and alterations are ascertainable by one skilled in theart and could be made without departing from the spirit and scopedisclosed herein.

To aid the Patent Office, and any readers of any patent issued on thisapplication in interpreting the claims appended hereto, applicants notethat they do not intend any of the appended claims to invoke 35 U.S.C. §112(f) as it exists on the date of filing hereof unless the words “meansfor” or “step for” are explicitly used in the particular claim.

1. A device configured to emulate a bidirectional string correlithmobject generator, comprising: a first processing stage that outputs afirst sub-string correlithm object comprising an n-bit digital word,wherein each bit of the n-bit digital word comprises a value of zero orone; a second processing stage communicatively coupled to the firstprocessing stage and that: receives the first sub-string correlithmobject from the first processing stage; receives a distance parameterrepresenting a distance in n-dimensional space between adjacentsub-string correlithm objects, the distance parameter identifying aparticular number of bits; for each bit of the first sub-stringcorrelithm object up to the particular number of bits identified by thedistance parameter, changes the value from a zero to a one or from a oneto a zero; and outputs a second sub-string correlithm object comprisingan n-bit digital word, wherein each bit of the second sub-stringcorrelithm object has a value that is based on the value of acorresponding bit of the first sub-string correlithm object and thechanged values for the particular number of bits identified by thedistance parameter; and a third processing stage communicatively coupledto the first processing stage and that: receives the first sub-stringcorrelithm object from the first processing stage; receives the distanceparameter; for each bit of the first sub-string correlithm object up tothe particular number of bits identified by the distance parameter,changes the value from a zero to a one or from a one to a zero; andoutputs a third sub-string correlithm object comprising an n-bit digitalword, wherein each bit of the third sub-string correlithm object has avalue that is based on the value of a corresponding bit of the firstsub-string correlithm object and the changed values for the particularnumber of bits identified by the distance parameter.
 2. The device ofclaim 1, wherein the second processing stage further: receives thesecond sub-string correlithm object as feedback from the secondprocessing stage; for each bit of the second sub-string correlithmobject up to the particular number of bits identified by the distanceparameter, changes the value from a zero to a one or from a one to azero; and outputs a fourth sub-string correlithm object comprising ann-bit digital word, wherein each bit of the fourth sub-string correlithmobject has a value that is based on the value of a corresponding bit ofthe second sub-string correlithm object and the changed values for theparticular number of bits identified by the distance parameter.
 3. Thedevice of claim 2, wherein the third processing stage further: receivesthe third sub-string correlithm object as feedback from the thirdprocessing stage; for each bit of the third sub-string correlithm objectup to the particular number of bits identified by the distanceparameter, changes the value from a zero to a one or from a one to azero; and outputs a fifth sub-string correlithm object comprising ann-bit digital word, wherein each bit of the fifth sub-string correlithmobject has a value that is based on the value of a corresponding bit ofthe third sub-string correlithm object and the changed values for theparticular number of bits identified by the distance parameter.
 4. Thedevice of claim 1, wherein the first processing stage receives adimension parameter as an input, the dimension parameter identifying thenumber of dimensions, n, in the n-bit digital word for each sub-stringcorrelithm object.
 5. The device of claim 1, wherein the firstsub-string correlithm object, the second sub-string correlithm object,and a third sub-string correlithm object form a first bidirectionalstring correlithm object in which the first sub-string correlithm objectprecedes and is adjacent to the second sub-string correlithm object andthe first sub-string correlithm object precedes and is adjacent to thethird sub-string correlithm object.
 6. The device of claim 5, whereinthe first processing stage and the second processing stage are eachconfigured to output additional sub-string correlithm objects based atleast in part upon the first sub-string correlithm object to form asecond bidirectional string correlithm object that has only the firstsub-string correlithm object in common with the first bidirectionalstring correlithm object.
 7. The device of claim 1, wherein the distanceparameter corresponds to one standard deviation of the n-dimensionalspace.
 8. The device of claim 1, wherein: the second processing stagesuccessively outputs a subsequent sub-string correlithm object based onchanging bit values of the immediately prior sub-string correlithmobject received as feedback; and the third processing stage successivelyoutputs a subsequent sub-string correlithm object based on changing bitvalues of the immediately prior sub-string correlithm object received asfeedback.
 9. The device of claim 1, wherein: the bits of the firstsub-string correlithm object that are changed in value for the secondsub-string correlithm object are selected randomly from the n-bitdigital word; and the bits of the first sub-string correlithm objectthat are changed in value for the third sub-string correlithm object areselected randomly from the n-bit digital word.
 10. The device of claim1, wherein: the first sub-string correlithm object has a first core; thesecond sub-string correlithm object has a second core; the thirdsub-string correlithm object has a third core; the first core overlapsin n-dimensional space with the second core; and the first core overlapsin n-dimensional space with the third core.
 11. A method for generatinga bidirectional string correlithm object, comprising: outputting at afirst processing stage a first sub-string correlithm object comprisingan n-bit digital word, wherein each bit of the n-bit digital wordcomprises a value of zero or one; receiving the first sub-stringcorrelithm object at a second processing stage; receiving at the secondprocessing stage a distance parameter representing a distance inn-dimensional space between adjacent sub-string correlithm objects, thedistance parameter identifying a particular number of bits; for each bitof the first sub-string correlithm object up to the particular number ofbits identified by the distance parameter, changing the value from azero to a one or from a one to a zero; outputting a second sub-stringcorrelithm object comprising an n-bit digital word, wherein each bit ofthe second sub-string correlithm object has a value that is based on thevalue of a corresponding bit of the first sub-string correlithm objectand the changed values for the particular number of bits identified bythe distance parameter; receiving the first sub-string correlithm objectat a third processing stage; receiving the distance parameter at thethird processing stage; for each bit of the first sub-string correlithmobject up to the particular number of bits identified by the distanceparameter, changing the value from a zero to a one or from a one to azero; outputting a third sub-string correlithm object comprising ann-bit digital word, wherein each bit of the third sub-string correlithmobject has a value that is based on the value of a corresponding bit ofthe first sub-string correlithm object and the changed values for theparticular number of bits identified by the distance parameter.
 12. Themethod of claim 11, further comprising: receiving at the secondprocessing stage the second sub-string correlithm object as feedback;for each bit of the second sub-string correlithm object up to theparticular number of bits identified by the distance parameter, changingthe value from a zero to a one or from a one to a zero; and outputting afourth sub-string correlithm object comprising an n-bit digital word,wherein each bit of the fourth sub-string correlithm object has a valuethat is based on the value of a corresponding bit of the secondsub-string correlithm object and the changed values for the particularnumber of bits identified by the distance parameter.
 13. The method ofclaim 12, further comprising: receiving at the third processing stagethe third sub-string correlithm object as feedback; for each bit of thethird sub-string correlithm object up to the particular number of bitsidentified by the distance parameter, changing the value from a zero toa one or from a one to a zero; and outputting a fifth sub-stringcorrelithm object comprising an n-bit digital word, wherein each bit ofthe fifth sub-string correlithm object has a value that is based on thevalue of a corresponding bit of the third sub-string correlithm objectand the changed values for the particular number of bits identified bythe distance parameter.
 14. The method of claim 11, further comprisingreceiving at the first processing stage a dimension parameter as aninput, the dimension parameter identifying the number of dimensions, n,in the n-bit digital word for each sub-string correlithm object.
 15. Themethod of claim 11, wherein the first sub-string correlithm object, thesecond sub-string correlithm object, and the third sub-string correlithmobject form a bidirectional string correlithm object in which the firstsub-string correlithm object precedes and is adjacent to the secondsub-string correlithm object and the first sub-string correlithm objectprecedes and is adjacent to the third sub-string correlithm object. 16.The method of claim 15, further comprising outputting additionalsub-string correlithm objects based at least in part upon the firstsub-string correlithm object to form a second bidirectional stringcorrelithm object that has only the first sub-string correlithm objectin common with the first bidirectional string correlithm object.
 17. Themethod of claim 11, wherein the distance parameter corresponds to onestandard deviation of the n-dimensional space.
 18. The method of claim11, wherein: the bits of the first sub-string correlithm object that arechanged in value for the second sub-string correlithm object areselected randomly from the n-bit digital word; and the bits of the firstsub-string correlithm object that are changed in value for the thirdsub-string correlithm object are selected randomly from the n-bitdigital word.
 19. The method of claim 11, wherein: the first sub-stringcorrelithm object has a first core; the second sub-string correlithmobject has a second core; the third sub-string correlithm object has athird core; the first core overlaps in n-dimensional space with thesecond core; and the first core overlaps in n-dimensional space with thethird core.
 20. A computer program comprising executable instructionsstored in a non-transitory computer readable medium such that whenexecuted by a processor causes the processor to emulate a bidirectionalstring correlithm object generator in a correlithm object processingsystem configured to: output at a first processing stage a firstsub-string correlithm object comprising an n-bit digital word, whereineach bit of the n-bit digital word comprises a value of zero or one;receive the first sub-string correlithm object from the first processingstage at a second processing stage; receive at the second processingstage a distance parameter representing a distance in n-dimensionalspace between adjacent sub-string correlithm objects, the distanceparameter identifying a particular number of bits; for each bit of thefirst sub-string correlithm object up to the particular number of bitsidentified by the distance parameter, change the value from a zero to aone or from a one to a zero; output a second sub-string correlithmobject comprising an n-bit digital word, wherein each bit of the secondsub-string correlithm object has a value that is based on the value of acorresponding bit of the first sub-string correlithm object and thechanged values for the particular number of bits identified by thedistance parameter; receive the first sub-string correlithm object at athird processing stage; receive the distance parameter at the thirdprocessing stage; for each bit of the first sub-string correlithm objectup to the particular number of bits identified by the distanceparameter, change the value from a zero to a one or from a one to azero; output a third sub-string correlithm object comprising an n-bitdigital word, wherein each bit of the third sub-string correlithm objecthas a value that is based on the value of a corresponding bit of thefirst sub-string correlithm object and the changed values for theparticular number of bits identified by the distance parameter.
 21. Thecomputer program of claim 20, further configured to: receive at thesecond processing stage the second sub-string correlithm object asfeedback; for each bit of the second sub-string correlithm object up tothe particular number of bits identified by the distance parameter,change the value from a zero to a one or from a one to a zero; andoutput a fourth sub-string correlithm object comprising an n-bit digitalword, wherein each bit of the fourth sub-string correlithm object has avalue that is based on the value of a corresponding bit of the secondsub-string correlithm object and the changed values for the particularnumber of bits identified by the distance parameter.
 22. The computerprogram of claim 21, further configured to: receive at the thirdprocessing stage the third sub-string correlithm object as feedback; foreach bit of the third sub-string correlithm object up to the particularnumber of bits identified by the distance parameter, change the valuefrom a zero to a one or from a one to a zero; and output a fifthsub-string correlithm object comprising an n-bit digital word, whereineach bit of the fifth sub-string correlithm object has a value that isbased on the value of a corresponding bit of the third sub-stringcorrelithm object and the changed values for the particular number ofbits identified by the distance parameter.